max_stars_repo_path
stringlengths 4
237
| max_stars_repo_name
stringlengths 6
117
| max_stars_count
int64 0
95.2k
| id
stringlengths 1
7
| content
stringlengths 12
593k
| input_ids
listlengths 7
549k
|
|---|---|---|---|---|---|
src/golem/__init__.py
|
aspuru-guzik-group/golem
| 10 |
101441
|
<gh_stars>1-10
from ._version import get_versions
__version__ = get_versions()['version']
del get_versions
from .golem import Golem
from .extensions import BaseDist, Delta, Normal, TruncatedNormal, FoldedNormal
from .extensions import Uniform, TruncatedUniform, BoundedUniform, Gamma
from .extensions import Poisson, DiscreteLaplace, Categorical
from .extensions import FrozenNormal, FrozenUniform, FrozenGamma
from .extensions import FrozenPoisson, FrozenDiscreteLaplace, FrozenCategorical
|
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29907,
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] |
fib.py
|
ednl/bigint
| 0 |
57140
|
<reponame>ednl/bigint
#!/usr/bin/env python3
from sys import argv
try:
N = int(argv[1])
except (IndexError, ValueError):
exit("Not a number")
a = 0
b = 1
for _ in range(N):
a, b = b, a + b
print(a)
|
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420,
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2
] |
BOOK/MAIN/05-file-handling/chapter-5/02-copy-atheletics.py
|
kabirsrivastava3/python-practice
| 0 |
1613339
|
# A file sports.dat contains information in following format :
#Event ~ Participant
#A function that would read contents from file sports.dat and creates a file named Atheletic.dat copying only those records from sports.dat where the event name is " Atheletics ".
def eventParticipant():
file1 = open("sports.txt","r")
file2 = open("Atheletics.txt","w")
listObj = file1.readlines()
for index in listObj:
print(index[:9])
if index[:9] == "atheletic" or index[:9] == "Atheletic":
file2.write(index)
file1.close()
file2.close()
eventParticipant()
|
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2
] |
platformer/ai.py
|
lukaszzalewski0001/2d_platformer
| 0 |
1600877
|
'''
Copyright 2019 <NAME>.
Licensed under the Apache License, Version 2.0 (the "License");
you may not use this file except in compliance with the License.
You may obtain a copy of the License at
http://www.apache.org/licenses/LICENSE-2.0
Unless required by applicable law or agreed to in writing, software
distributed under the License is distributed on an "AS IS" BASIS,
WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
See the License for the specific language governing permissions and
limitations under the License.
'''
import math
import random
class EnemyAI:
'''Creates EnemyAI object, that controls ai\'s behaviour'''
def __init__(self, player, movement, ai_scheme, ai_level):
self.player = player
self.movement = movement
self.ai_scheme = ai_scheme
self.ai_level = ai_level
self.weapon = None
def tick(self):
'''
Runs enemy's ai depending on ai_scheme value
ai_scheme = 1:
ai moves left and right
ai_scheme = 2:
ai moves left and right, but also jumps
ai_scheme = 3:
ai moves towards player
ai_scheme = 4:
ai moves and jumps towards player
'''
if self.ai_scheme == 1:
self.__dumb_moving_left_right_ai()
elif self.ai_scheme == 2:
self.__full_dumb_movement_ai()
elif self.ai_scheme == 3:
self.__moving_to_player_ai()
elif self.ai_scheme == 4:
self.__full_movement_ai()
def shooting_ai(self):
'''
Responds for enemy's shooting depending on ai_level
ai_level = 0:
ai shoots in random direction
ai_level = 1:
ai shoots towards player
'''
if self.ai_level == 0:
self.weapon.shoot([self.movement.enemy_pos[0] - 16, self.movement.enemy_pos[1]], math.radians(random.randrange(360)))
if self.ai_level == 1:
angle = math.atan2(self.player.player_pos[1] - self.movement.enemy_pos[1],
self.player.player_pos[0] - self.movement.enemy_pos[0])
self.weapon.shoot([self.movement.enemy_pos[0] - 16, self.movement.enemy_pos[1]], angle)
def __dumb_moving_left_right_ai(self):
'''AI moves left and right'''
self.movement.moving = True
if (self.movement.enemy_pos[0] == 0 and self.movement.move_dir == 0) or self.movement.map_.is_any_solid_block_left_from_entity(self.movement.enemy_pos):
self.movement.move_dir = 1
elif (self.movement.enemy_pos[0] == (self.movement.map_.map_size - 1) * 32 and self.movement.move_dir == 1) or self.movement.map_.is_any_solid_block_right_from_entity(self.movement.enemy_pos):
self.movement.move_dir = 0
def __full_dumb_movement_ai(self):
'''AI moves left and right, but also jumps'''
self.__dumb_moving_left_right_ai()
if not self.movement.jumping:
self.movement.jump_current_speed = self.movement.jump_max_speed
self.movement.jumping = True
self.movement.jump_dir = 0
def __moving_to_player_ai(self):
'''AI moves towards player'''
self.movement.moving = True
if abs(self.player.player_pos[0] - self.movement.enemy_pos[0]) <= 8:
self.movement.moving = False
elif self.player.player_pos[0] < self.movement.enemy_pos[0]:
self.movement.move_dir = 0
elif self.player.player_pos[0] > self.movement.enemy_pos[0]:
self.movement.move_dir = 1
def __full_movement_ai(self):
'''AI moves and jumps towards player'''
self.__moving_to_player_ai()
if not self.movement.jumping:
if self.player.player_pos[1] < self.movement.enemy_pos[1]:
self.movement.jump_current_speed = self.movement.jump_max_speed
self.movement.jumping = True
self.movement.jump_dir = 0
|
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] |
Python/maxArea.py
|
alexwu2021/practice
| 0 |
164482
|
<reponame>alexwu2021/practice
class Solution(object):
def maxArea(self, height):
"""
:type height: List[int]
:rtype: int
"""
n = len(height)
d = [0] * n
for i in range(n):
d[i] = height[i]
for i in range(1, n):
avg = d[i-1] / (i * 1.0)
if height[i] >= avg:
d[i] = max(d[i], d[i-1], d[i-1] + avg)
else:
d[i] = max(d[i], d[i-1], height[i] * (1 + d[i-1]/(avg *1.0)))
for j in range(n):
print('d[%d]:%d' % (j, d[j]))
return d[n-1]
sol = Solution()
height = [10, 8, 0, 7, 7, 9, 9]
ret = sol.maxArea(height)
print('ret: %d' % ret)
|
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1273,
3240,
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13,
2
] |
bc/documents/models.py
|
Buckinghamshire-Digital-Service/buckinghamshire-council
| 1 |
155177
|
<gh_stars>1-10
from django.db import models
from wagtail.documents.models import AbstractDocument
from wagtail.documents.models import Document as WagtailDocument
class CustomDocument(AbstractDocument):
talentlink_attachment_id = models.IntegerField(blank=True, null=True)
admin_form_fields = WagtailDocument.admin_form_fields + (
"talentlink_attachment_id",
)
|
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] |
majortom_gateway/__init__.py
|
grantperry/majortom_gateway_package
| 0 |
114098
|
from majortom_gateway.gateway_api import GatewayAPI
name = "majortom_gateway"
|
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290,
29918,
17062,
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29908,
13,
2
] |
dataset/gla_vol_time_series/cumul_to_rates_rgi01regions.py
|
subond/ww_tvol_study
| 20 |
36802
|
<gh_stars>10-100
import os, sys
import pandas as pd
import numpy as np
import pyddem.tdem_tools as tt
# example to integrate the RGI-O1 cumulative time series into rates with time-varying glacier areas
# file with time-varying areas for RGI regions
fn_tarea = '/home/atom/data/inventory_products/RGI/tarea_zemp.csv'
# list of regional cumulative series
reg_dir = '/home/atom/ongoing/work_worldwide/vol/final'
list_fn_reg= [os.path.join(reg_dir,'dh_'+str(i).zfill(2)+'_rgi60_int_base_reg.csv') for i in [1,2,3,4,5,6,7,8,9,10,11,12,13,14,15,16,17,18,19]]
# periods of interest
tlim_00_10 = [np.datetime64('2000-01-01'),np.datetime64('2010-01-01')]
tlim_10_20 = [np.datetime64('2010-01-01'),np.datetime64('2020-01-01')]
tlim_ar6 = [np.datetime64('2006-01-01'),np.datetime64('2019-01-01')]
tlim_00_20 = [np.datetime64('2000-01-01'),np.datetime64('2020-01-01')]
list_tlim = [tlim_00_10,tlim_10_20,tlim_ar6,tlim_00_20]
list_tag = ['decad1','decad2','ar6','full']
# integrate the cumulative series into rates for each period and region
list_df = []
for fn_reg in list_fn_reg:
df_reg = pd.read_csv(fn_reg)
df_agg = tt.aggregate_all_to_period(df_reg,list_tlim=list_tlim,fn_tarea=fn_tarea,frac_area=1,list_tag=list_tag)
list_df.append(df_agg)
# concatenate results
df = pd.concat(list_df)
# convert m w.e. yr-1 into kg m-2 yr-1
df.dmdtda *= 1000
df.err_dmdtda *= 1000
# keep only variables of interests
df = df[['reg','period','dmdt','err_dmdt','dmdtda','err_dmdtda']]
df.to_csv('/home/atom/ongoing/work_ipcc_ar6/table_hugonnet_regions_10yr_20yr_ar6period_final.csv',float_format='%.2f',index=None)
|
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] |
courseproject/main.py
|
hpaldan/IOT_SummerCourseProject_2021
| 0 |
76024
|
#from mqtt import MQTTClient
from mqtt import MQTTClient_lib as MQTTClient
from network import WLAN
import machine
from machine import Pin
import time
import math
import config
adc = machine.ADC()
apin = adc.channel(pin='P19')
wlan = WLAN(mode=WLAN.STA)
wlan.connect(config.WIFI_NAME, auth=(WLAN.WPA2, config.WIFI_PW), timeout=5000)
while not wlan.isconnected():
machine.idle()
print("Connected to WiFi\n")
client = MQTTClient(config.PYCOM_NAME, config.SERVER_NAME,user=config.ADA_USERNAME, password= <PASSWORD>.<PASSWORD>, port=1883)
client.set_callback(sub_cb)
client.connect()
while True:
milliVal = apin.voltage()
celsius = (milliVal - 500.0) / 10.0 + 62
print('celsius is:' +str(celsius))
client.publish(topic=config.TOPIC_NAME, msg=str(celsius))
time.sleep(60*10)
|
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] |
tapioca_zendesk/tapioca_zendesk.py
|
nossas/bonde-zendesk
| 0 |
156395
|
<reponame>nossas/bonde-zendesk<filename>tapioca_zendesk/tapioca_zendesk.py
# coding: utf-8
from tapioca import (
TapiocaAdapter, generate_wrapper_from_adapter, JSONAdapterMixin)
from .resource_mapping import RESOURCE_MAPPING
import os
ZENDESK_API_ROOT = os.getenv('ZENDESK_API_ROOT')
assert ZENDESK_API_ROOT is not None, 'ZENDESK_API_ROOT not environment.'
class ZendeskClientAdapter(JSONAdapterMixin, TapiocaAdapter):
api_root = ZENDESK_API_ROOT
resource_mapping = RESOURCE_MAPPING
def get_request_kwargs(self, api_params, *args, **kwargs):
params = super(ZendeskClientAdapter, self).get_request_kwargs(
api_params, *args, **kwargs)
auth = (api_params.get('user') + '/token', api_params.get('password'))
params['auth'] = auth
return params
def get_iterator_list(self, response_data):
return response_data
def get_iterator_next_request_kwargs(self, iterator_request_kwargs,
response_data, response):
pass
Zendesk = generate_wrapper_from_adapter(ZendeskClientAdapter)
|
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core/utils.py
|
decosterkevin/foodtrack-back
| 0 |
90001
|
<reponame>decosterkevin/foodtrack-back
from geopy.geocoders import OpenCage
from django.conf import settings
from enum import Enum
def get_location_from_address(address):
geolocator = OpenCage(api_key=settings.OPENCAGE_API_KEY)
location = geolocator.geocode(address)
if location.latitude and location.longitude:
return (location.latitude, location.longitude)
else:
return None
class ChoiceEnum(Enum):
@classmethod
def choices(cls):
return [(choice.name, choice.value) for choice in cls]
|
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7348,
297,
1067,
29879,
29962,
2
] |
my-python-lab/ExponentnumApp.py
|
baizoukou/my-python-lab
| 0 |
75513
|
<filename>my-python-lab/ExponentnumApp.py<gh_stars>0
## creat fct
def raise_to_power(base_num, pow_num): ## fct take 2 input num
result = 1 ## def var call result is going to store result
for index in range(pow_num): ## specify for loop that loop through range of num
result = result * base_num ## math is going to be store in result
return result
print(raise_to_power(2,3))
|
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jobs/jobs/celeryp.py
|
gitdaniel228/jobSearch
| 0 |
155849
|
<reponame>gitdaniel228/jobSearch
import os
from celery import Celery
os.environ.setdefault('DJANGO_SETTINGS_MODULE', 'jobs.settings.production')
from django.conf import settings
app = Celery('jobs')
app.config_from_object('django.conf:settings')
app.autodiscover_tasks(lambda: settings.INSTALLED_APPS)
|
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] |
tests/__init__.py
|
vpv11110000/pyss
| 0 |
52769
|
<reponame>vpv11110000/pyss<gh_stars>0
# -*- coding: utf-8 -*-
import sys
import os
import random
import unittest
sys.path.append(os.path.abspath(os.path.join(os.path.dirname(__file__), '..')))
DIRNAME_MODULE = os.path.dirname(os.path.dirname(os.path.dirname(os.path.realpath(sys.argv[0])))) + os.sep
sys.path.append(DIRNAME_MODULE)
sys.path.append(DIRNAME_MODULE + "pyss" + os.sep)
from pyss import *
def pyss_module_suite():
#test_files = glob.glob('test_*.py')
#module_strings = [test_file[0:len(test_file)-3] for test_file in test_files]
#suites = [unittest.defaultTestLoader.loadTestsFromName(test_file) for test_file in module_strings]
#test_suite = unittest.TestSuite(suites)
testmodules=["test_advance",
"test_assemble",
"test_assign",
"test_enter_leave",
"test_generate",
"test_pyss_model",
"test_queue",
"test_recurrent_statistic",
"test_segment",
"test_split",
"test_statisticalseries",
"test_storage",
"test_table",
"test_terminate",
"test_transfer",
]
loader = unittest.TestLoader()
suite = unittest.TestSuite()
for testMopduleName in testmodules:
try:
# If the module defines a suite() function, call it to get the suite.
mod = __import__(testMopduleName, globals(), locals(), ['suite'])
suitefn = getattr(mod, 'suite')
suite.addTest(suitefn())
except (ImportError, AttributeError):
# else, just load all the test cases from the module.
suite.addTest(unittest.defaultTestLoader.loadTestsFromName(testMopduleName))
return suite
if __name__ == '__main__':
suite=pyss_module_suite()
unittest.TextTestRunner().run(suite)
|
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] |
neural_lambda_calculus/__init__.py
|
brandontrabucco/neural_lambda_calculus
| 0 |
150893
|
"""Author: <NAME>, <NAME>, Copyright 2019"""
|
[
1,
9995,
13720,
29901,
529,
5813,
10202,
529,
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14187,
1266,
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29929,
15945,
29908,
2
] |
caf_verilog/cpx_multiply.py
|
chiranthsiddappa/caf_verilog
| 1 |
140423
|
from .quantizer import quantize
from .io_helper import write_quantized_output
from numpy import floor
import os
from jinja2 import Environment, FileSystemLoader
from shutil import copy
from .caf_verilog_base import CafVerilogBase
class CpxMultiply(CafVerilogBase):
def __init__(self, x, y, x_i_bits=12, x_q_bits=0, y_i_bits=12, y_q_bits=0, output_dir='.'):
"""
:param x:
:param y:
:param x_i_bits: Bit length for real values
:param x_q_bits: Bit length for imaginary values
:param y_i_bits: Bit length for real values
:param y_q_bits: Bit length for imaginary values
:param output_dir: Directory to place modules and test files
"""
self.x = x
self.y = y
self.cpx_input_length_check()
self.x_i_bits = x_i_bits
self.x_q_bits = x_q_bits if x_q_bits else self.x_i_bits
self.y_i_bits = y_i_bits
self.y_q_bits = y_q_bits if y_q_bits else self.y_i_bits
self.x_quant = quantize(self.x, self.x_i_bits, self.x_q_bits)
self.y_quant = quantize(self.y, self.y_i_bits, self.y_q_bits)
self.output_dir = output_dir
self.tb_filename = 'cpx_multiply_tb.v'
self.test_value_filename = 'cpx_multiply_input_values.txt'
self.test_output_filename = 'cpx_multiply_output_values.txt'
copy(self.module_path(), self.output_dir)
def gen_tb(self):
"""
Generate a test bench using quantized values.
:return:
"""
write_quantized_output(self.output_dir, self.test_value_filename, self.x_quant, self.y_quant)
self.write_cpx_multiply_tb_module()
def gen_quantized_output(self):
"""
Perform the multiplication and then quantize to the closest representation of what the verilog module should
produce for the given bit length.
:return:
"""
"""
x y
(x + yi)(u + vi) = (xu - yv) + (xv + yu)i
"""
xu = floor(self.x_quant.real) * floor(self.y_quant.real)
yv = floor(self.x_quant.imag) * floor(self.y_quant.imag)
xv = floor(self.x_quant.real) * floor(self.y_quant.imag)
yu = floor(self.x_quant.imag) * floor(self.y_quant.real)
i_sub = xu - yv
y_add = xv + yu
final_out = i_sub + y_add*1j
return final_out
def write_cpx_multiply_tb_module(self):
"""
Write out a testbench file to test the cpx_multiply module.
:return:
"""
out_tb = None
t_dict = self.template_dict("cpx_multiply_tb")
template_loader = FileSystemLoader(searchpath=self.tb_module_path())
env = Environment(loader=template_loader)
template = env.get_template('cpx_multiply_tb.v')
out_tb = template.render(**t_dict)
with open(os.path.join(self.output_dir, self.tb_filename), 'w+') as tb_file:
tb_file.write(out_tb)
def template_dict(self, inst_name=None):
t_dict = {'xi_bits': self.x_i_bits, 'xq_bits': self.x_q_bits, 'yi_bits': self.y_i_bits,
'yq_bits': self.y_q_bits}
t_dict['i_out_bits'] = self.x_i_bits + self.y_i_bits
t_dict['q_out_bits'] = self.x_q_bits + self.y_q_bits
t_dict['cpx_multiply_input'] = os.path.abspath(os.path.join(self.output_dir, self.test_value_filename))
t_dict['cpx_multiply_output'] = os.path.abspath(os.path.join(self.output_dir, self.test_output_filename))
t_dict['cpx_multiply_name'] = inst_name if inst_name else 'cpx_multiply_tb'
return t_dict
|
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22625,
29915,
13,
4706,
736,
260,
29918,
8977,
13,
2
] |
user_service/user_service/api.py
|
Ziang-Lu/Flask-Blog
| 0 |
775
|
<filename>user_service/user_service/api.py
# -*- coding: utf-8 -*-
"""
API definition module.
"""
from flask import Blueprint
from flask_restful import Api
from .resources.user import UserAuth, UserItem, UserList, UserFollow
# Create an API-related blueprint
api_bp = Blueprint(name='api', import_name=__name__)
api = Api(api_bp)
api.add_resource(UserList, '/users')
api.add_resource(UserItem, '/users/<int:id>')
api.add_resource(UserAuth, '/user-auth')
api.add_resource(
UserFollow, '/user-follow/<int:follower_id>/<followed_username>'
)
|
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287,
29918,
6786,
16299,
13,
29897,
13,
2
] |
zerojudge/a080.py
|
x06lan/mt
| 0 |
126070
|
# from _typeshed import Self
import sys
input_text=[]
try :
while True:
tem=input()
if tem=="":
break
input_text.append(tem)
except:
pass
tree_data={}
class newtree():
def __init__(self):
self.tree={}
self.node=1
def tree_add_text(self,mom,input):
# print(input)
try:
index=mom[input[0]]
except:
index=None
if index == None :
mom[input[0]]={}
self.node+=1
if len(input) != 1:
# print(mom)
self.tree_add_text(mom[input[0]],input[1::1])
else :
self.tree=mom
tree=newtree()
tree_data={}
for x in input_text:
tree.tree_add_text(tree_data,x)
print(tree_data)
print(tree.node)
|
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29918,
1272,
29897,
13,
2158,
29898,
8336,
29889,
3177,
29897,
13,
13,
2
] |
instaclient/instagram/postmedia.py
|
pthalin/instaclient
| 0 |
37647
|
from typing import Optional, List, TYPE_CHECKING
if TYPE_CHECKING:
from instaclient.client.instaclient import InstaClient
from instaclient.instagram.instaobject import InstaBaseObject
class PostMedia(InstaBaseObject):
def __init__(self,
client:'InstaClient',
id:int,
type:str,
viewer:str,
shortcode:str,
src_url:str,
is_video:bool,
accessibility_caption:Optional[str]=None,
tagged_users:Optional[List[str]]=None,
# If Media is Video
has_audio:Optional[bool]=None,
video_duration:Optional[float]=None,
video_view_count:Optional[int]=None,
**kwargs
) -> None:
super().__init__(client, id, type, viewer)
self.shortcode = shortcode
self.src_url = src_url
self.is_video = is_video
self.accessibility_caption = accessibility_caption
self.tagged_users = tagged_users
# IF Media is Video
self.has_audio = has_audio
self.video_duration = video_duration
self.video_view_count = video_view_count
def __repr__(self) -> str:
return f'PostMedia<{self.shortcode}>'
def __eq__(self, o: object) -> bool:
if isinstance(o, PostMedia):
if o.shortcode == self.shortcode:
return True
return False
|
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5852,
13,
4706,
736,
7700,
2
] |
fiware_cloto/cloto/tests/acceptance/commons/configuration.py
|
telefonicaid/fiware-cloto
| 2 |
112281
|
<reponame>telefonicaid/fiware-cloto<gh_stars>1-10
#!/usr/bin/env python
# -*- encoding: utf-8 -*-
#
# Copyright 2014 Telefónica Investigación y Desarrollo, S.A.U
#
# This file is part of FI-WARE project.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
#
# You may obtain a copy of the License at:
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS, WITHOUT
# WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
#
# See the License for the specific language governing permissions and
# limitations under the License.
#
# For those usages not covered by the Apache version 2.0 License please
# contact with <EMAIL>
#
__author__ = 'arobres'
REST_PATH = '../../../../manage.py'
POLICY_MANAGER_IP = 'fiwarecloto'
POLICY_MANAGER_PORT = 8000
FACTS_IP = 'fiwarecloto'
FACTS_PORT = 5000
RABBIT_IP = 'rabbitmq'
AUTH_TOKEN_OLD = ''
KEYSTONE_URL = ''
TENANT_ID = ''
TENANT_NAME = ''
USER = ''
PASSWORD = ''
CONTENT_TYPE = 'application/json'
HEADERS = {'content-type': CONTENT_TYPE, 'X-Auth-Token': ''}
DB_PATH = '../../../../cloto.db'
MOCK_IP = u'127.0.0.1'
MOCK_PORT = 8080
MOCK_PATH = u'commons/server_mock.py'
|
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2
] |
test_ensemble.py
|
Sette/CoNAS
| 0 |
190425
|
<reponame>Sette/CoNAS<filename>test_ensemble.py
# -*- coding: utf-8 -*-
"""test_ensemble.ipynb
Automatically generated by Colaboratory.
Original file is located at
https://colab.research.google.com/drive/1Y0wUtSeRwwUQm2tJneNIcHT_C_cxRJiY
"""
path_logits_darts = "cnn/"
path_logits1 = "cnn_space1/"
path_logits2 = "cnn_space2/"
path_logits3 = "cnn_space3"
import pickle
import os
import sys
import glob
import json
import numpy as np
import torch
import utils
import logging
import argparse
import pickle
import time
import torch.nn as nn
import torch.utils
import torchvision.datasets as dset
import torch.backends.cudnn as cudnn
from torch.autograd import Variable
import torchvision
import torchvision.transforms as transforms
save = path_logits+'/test-{}-{}'.format("ENS", time.strftime("%Y%m%d-%H%M%S"))
utils.create_exp_dir(save, scripts_to_save=glob.glob('*.py'))
log_format = '%(asctime)s %(message)s'
logging.basicConfig(stream=sys.stdout, level=logging.INFO,
format=log_format, datefmt='%m/%d %I:%M:%S %p')
fh = logging.FileHandler(os.path.join(save, 'log.txt'))
fh.setFormatter(logging.Formatter(log_format))
logging.getLogger().addHandler(fh)
class Cutout(object):
def __init__(self, length):
self.length = length
def __call__(self, img):
h, w = img.size(1), img.size(2)
mask = np.ones((h, w), np.float32)
y = np.random.randint(h)
x = np.random.randint(w)
y1 = np.clip(y - self.length // 2, 0, h)
y2 = np.clip(y + self.length // 2, 0, h)
x1 = np.clip(x - self.length // 2, 0, w)
x2 = np.clip(x + self.length // 2, 0, w)
mask[y1: y2, x1: x2] = 0.
mask = torch.from_numpy(mask)
mask = mask.expand_as(img)
img *= mask
return img
def _data_transforms_cifar10(cutout,cutout_length):
CIFAR_MEAN = [0.49139968, 0.48215827, 0.44653124]
CIFAR_STD = [0.24703233, 0.24348505, 0.26158768]
train_transform = transforms.Compose([
transforms.RandomCrop(32, padding=4),
transforms.RandomHorizontalFlip(),
transforms.ToTensor(),
transforms.Normalize(CIFAR_MEAN, CIFAR_STD),
])
if cutout:
train_transform.transforms.append(Cutout(cutout_length))
valid_transform = transforms.Compose([
transforms.ToTensor(),
transforms.Normalize(CIFAR_MEAN, CIFAR_STD),
])
return train_transform, valid_transform
_, test_transform = _data_transforms_cifar10(True,16)
test_data = dset.CIFAR10(root="../data", train=False, download=True, transform=test_transform)
test_queue = torch.utils.data.DataLoader(test_data, batch_size=96, shuffle=False, pin_memory=True, num_workers=2)
targets_all = []
for step, (input, target) in enumerate(test_queue):
input = Variable(input, volatile=True).cuda()
targets = Variable(target, volatile=True).cuda(async=True)
targets = targets.cpu().detach().numpy()
targets_all.extend(targets)
#print(targets)
from sklearn.metrics import accuracy_score
from sklearn.metrics import confusion_matrix
import copy
acc_darts_all = []
acc_1_all = []
acc_2_all = []
acc_3_all = []
acc_ensemble_all = []
for x in range(50):
logits_darts = pickle.load( open( path_logits_darts + "/logits_"+str(x)+".p", "rb" ) )
print(type(logits_darts))
logits1 = pickle.load( open( path_logits1 + "/logits_"+str(x)+".p", "rb" ) )
logits2 = pickle.load( open( path_logits2 + "/logits_"+str(x)+".p", "rb" ) )
logits3 = pickle.load( open( path_logits3 + "/logits_"+str(x)+".p", "rb" ) )
logits = copy.deepcopy(logits1)
for i in range(len(logits)):
for j in range(len(logits[i])):
logits[i][j] = logits[i][j] + logits2[i][j] + logits3[i][j]
predicted_darts_all = []
for out in logits_darts:
_, predicted = torch.max(out.data, 1)
preds = predicted.cpu().detach().numpy()
for pred in preds:
predicted_darts_all.append(pred)
predicted1_all = []
for out in logits1:
_, predicted = torch.max(out.data, 1)
preds = predicted.cpu().detach().numpy()
for pred in preds:
predicted1_all.append(pred)
predicted2_all = []
for out in logits2:
_, predicted = torch.max(out.data, 1)
preds = predicted.cpu().detach().numpy()
for pred in preds:
predicted2_all.append(pred)
predicted3_all = []
for out in logits3:
_, predicted = torch.max(out.data, 1)
preds = predicted.cpu().detach().numpy()
for pred in preds:
predicted3_all.append(pred)
predicted_all = []
for out in logits:
_, predicted = torch.max(out.data, 1)
preds = predicted.cpu().detach().numpy()
for pred in preds:
predicted_all.append(pred)
acc_darts_all.append(100*accuracy_score(predicted_darts_all,targets_all))
acc_1_all.append(100*accuracy_score(predicted1_all,targets_all))
acc_2_all.append(100*accuracy_score(predicted2_all,targets_all))
acc_3_all.append(100*accuracy_score(predicted3_all,targets_all))
acc_ensemble_all.append(100*accuracy_score(predicted_all,targets_all))
#prec1, prec5 = accuracy(predicted_all, target_all)
logging.info('test acc space 1 epoch %d %f',x,100* accuracy_score(predicted1_all,targets_all))
logging.info('test acc space 2 epoch %d %f',x,100* accuracy_score(predicted2_all,targets_all))
logging.info('test acc space 3 epoch %d %f',x,100* accuracy_score(predicted3_all,targets_all))
logging.info('test ensemble acc epoch %d %f',x,100* accuracy_score(predicted_all,targets_all))
logging.info('test darts acc epoch %d %f',x, 100*accuracy_score(predicted_darts_all,targets_all))
|
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2
] |
morphablegraphs/motion_generator/graph_walk.py
|
dfki-asr/morphablegraphs
| 5 |
125464
|
#!/usr/bin/env python
#
# Copyright 2019 DFKI GmbH.
#
# Permission is hereby granted, free of charge, to any person obtaining a
# copy of this software and associated documentation files (the
# "Software"), to deal in the Software without restriction, including
# without limitation the rights to use, copy, modify, merge, publish,
# distribute, sublicense, and/or sell copies of the Software, and to permit
# persons to whom the Software is furnished to do so, subject to the
# following conditions:
#
# The above copyright notice and this permission notice shall be included
# in all copies or substantial portions of the Software.
#
# THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS
# OR IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF
# MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN
# NO EVENT SHALL THE AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM,
# DAMAGES OR OTHER LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR
# OTHERWISE, ARISING FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE
# USE OR OTHER DEALINGS IN THE SOFTWARE.
"""
Created on Tue Jul 14 18:39:41 2015
@author: <NAME>
"""
from datetime import datetime
import collections
import json
import numpy as np
from anim_utils.animation_data import MotionVector, align_quaternion_frames
from anim_utils.animation_data.motion_concatenation import align_and_concatenate_frames
from anim_utils.utilities.log import write_log, write_message_to_log, LOG_MODE_DEBUG, LOG_MODE_INFO, LOG_MODE_ERROR
from .annotated_motion_vector import AnnotatedMotionVector
from ..constraints.spatial_constraints import SPATIAL_CONSTRAINT_TYPE_KEYFRAME_POSITION, SPATIAL_CONSTRAINT_TYPE_TWO_HAND_POSITION
from .keyframe_event_list import KeyframeEventList
from ..constraints.spatial_constraints.splines.utils import plot_annotated_spline
DEFAULT_PLACE_ACTION_LIST = ["placeRight", "placeLeft","insertRight","insertLeft","screwRight", "screwLeft"] #list of actions in which the orientation constraints are ignored
class GraphWalkEntry(object):
def __init__(self, motion_state_graph, node_key, parameters, arc_length, start_frame, end_frame, motion_primitive_constraints=None):
self.node_key = node_key
self.parameters = parameters
self.arc_length = arc_length
self.start_frame = start_frame
self.end_frame = end_frame
self.motion_primitive_constraints = motion_primitive_constraints
self.n_spatial_components = motion_state_graph.nodes[node_key].get_n_spatial_components()
self.n_time_components = motion_state_graph.nodes[node_key].get_n_time_components()
@staticmethod
def from_json(motion_state_graph, data):
return GraphWalkEntry(motion_state_graph, tuple(data["node_key"]),
np.array(data["parameters"]), data["arc_length"],
data["start_frame"], data["end_frame"])
def to_json(self):
data = dict()
data["node_key"] =self.node_key
data["parameters"] = self.parameters.tolist()
data["arc_length"] = self.arc_length
data["start_frame"] = self.start_frame
data["end_frame"] = self.end_frame
return data
class HighLevelGraphWalkEntry(object):
def __init__(self, action_name, start_step, end_step, action_constraints):
self.action_name = action_name
self.start_step = start_step
self.end_step = end_step
self.action_constraints = action_constraints
class GraphWalk(object):
""" Product of the MotionGenerate class. Contains the graph walk used to generate the frames,
a mapping of frame segments
to elementary actions and a list of events for certain frames.
"""
def __init__(self, motion_state_graph, mg_input, algorithm_config, start_pose=None, create_ca_vis_data=False):
self.elementary_action_list = []
self.steps = []
self.motion_state_graph = motion_state_graph
self.step_count = 0
self.mg_input = mg_input
self._algorithm_config = algorithm_config
self.motion_vector = MotionVector(self.motion_state_graph.skeleton, algorithm_config)
if start_pose is None:
start_pose = mg_input.get_start_pose()
self.motion_vector.start_pose = start_pose
smoothing_settings = algorithm_config["smoothing_settings"]
self.spatial_smoothing_method = "smoothing"
self.apply_smoothing = smoothing_settings["spatial_smoothing"] # set whether the exported motion is smoothed at transitions
if "spatial_smoothing_method" in smoothing_settings:
self.spatial_smoothing_method = smoothing_settings["spatial_smoothing_method"]
self.motion_vector.apply_spatial_smoothing = False # deactivate smoothing during the synthesis
self.use_time_parameters = algorithm_config["activate_time_variation"]
self.constrain_place_orientation = algorithm_config["inverse_kinematics_settings"]["constrain_place_orientation"]
write_message_to_log("Use time parameters" + str(self.use_time_parameters), LOG_MODE_DEBUG)
self.keyframe_event_list = KeyframeEventList(create_ca_vis_data)
self.place_action_list = DEFAULT_PLACE_ACTION_LIST
def add_entry_to_action_list(self, action_name, start_step, end_step, action_constraints):
self.elementary_action_list.append(HighLevelGraphWalkEntry(action_name, start_step, end_step, action_constraints))
def convert_to_annotated_motion(self, step_size=1.0):
self.motion_vector.apply_spatial_smoothing = self.apply_smoothing # set wether or not smoothing is applied
self.motion_vector.spatial_smoothing_method = self.spatial_smoothing_method
self.convert_graph_walk_to_quaternion_frames(use_time_parameters=self.use_time_parameters, step_size=step_size)
self.keyframe_event_list.update_events(self, 0)
annotated_motion_vector = AnnotatedMotionVector(self.motion_state_graph.skeleton, self._algorithm_config)
annotated_motion_vector.frames = self.motion_vector.frames
annotated_motion_vector.n_frames = self.motion_vector.n_frames
annotated_motion_vector.frame_time = self.motion_state_graph.skeleton.frame_time
annotated_motion_vector.keyframe_event_list = self.keyframe_event_list
annotated_motion_vector.skeleton = self.motion_state_graph.skeleton
annotated_motion_vector.mg_input = self.mg_input
version = 0
if "version" in self._algorithm_config["inverse_kinematics_settings"]:
version = self._algorithm_config["inverse_kinematics_settings"]["version"]
if version == 1:
annotated_motion_vector.ik_constraints = self._create_ik_constraints()
elif version == 2:
annotated_motion_vector.ik_constraints = self._create_ik_constraints2()
annotated_motion_vector.graph_walk = self
return annotated_motion_vector
def get_action_from_keyframe(self, keyframe):
found_action_index = -1
step_index = self.get_step_from_keyframe(keyframe)
write_message_to_log("Found keyframe in step " + str(step_index), LOG_MODE_DEBUG)
if step_index < 0:
return found_action_index
for action_index, action in enumerate(self.elementary_action_list):
if action.start_step <= step_index <= action.end_step:
found_action_index = action_index
return found_action_index
def get_step_from_keyframe(self, keyframe):
found_step_index = -1
for step_index, step in enumerate(self.steps):
#Note the start_frame and end_frame are warped in update_temp_motion_vector
#print step.start_frame, keyframe, step.end_frame
if step.start_frame <= keyframe <= step.end_frame:
found_step_index = step_index
return found_step_index
def convert_graph_walk_to_quaternion_frames(self, start_step=0, use_time_parameters=False, step_size=1.0):
"""
:param start_step:
:return:
"""
if start_step == 0:
start_frame = 0
else:
start_frame = self.steps[start_step].start_frame
self.motion_vector.clear(end_frame=start_frame)
for step in self.steps[start_step:]:
step.start_frame = start_frame
#write_log(step.node_key, len(step.parameters))
quat_frames = self.motion_state_graph.nodes[step.node_key].back_project(step.parameters, use_time_parameters, step_size).get_motion_vector()
if step.node_key[1].lower().endswith("leftstance"):
foot_joint = "foot_r"
elif step.node_key[1].lower().endswith("rightstance"):
foot_joint = "foot_l"
else:
foot_joint = None
self.motion_vector.append_frames(quat_frames, foot_joint)
step.end_frame = self.get_num_of_frames()-1
start_frame = step.end_frame + 1
def get_global_spatial_parameter_vector(self, start_step=0):
initial_guess = []
for step in self.steps[start_step:]:
initial_guess += step.parameters[:step.n_spatial_components].tolist()
return initial_guess
def get_global_time_parameter_vector(self, start_step=0):
initial_guess = []
for step in self.steps[start_step:]:
initial_guess += step.parameters[step.n_spatial_components:].tolist()
return initial_guess
def update_spatial_parameters(self, parameter_vector, start_step=0):
write_message_to_log("Update spatial parameters", LOG_MODE_DEBUG)
offset = 0
for step in self.steps[start_step:]:
new_alpha = parameter_vector[offset:offset+step.n_spatial_components]
step.parameters[:step.n_spatial_components] = new_alpha
offset += step.n_spatial_components
def update_time_parameters(self, parameter_vector, start_step, end_step):
offset = 0
for step in self.steps[start_step:end_step]:
new_gamma = parameter_vector[offset:offset+step.n_time_components]
step.parameters[step.n_spatial_components:] = new_gamma
offset += step.n_time_components
def append_quat_frames(self, new_frames):
self.motion_vector.append_frames(new_frames)
def get_quat_frames(self):
return self.motion_vector.frames
def get_num_of_frames(self):
return self.motion_vector.n_frames
def update_frame_annotation(self, action_name, start_frame, end_frame):
""" Adds a dictionary to self.frame_annotation marking start and end
frame of an action.
"""
self.keyframe_event_list.update_frame_annotation(action_name, start_frame, end_frame)
def _create_ik_constraints(self):
ik_constraints = []
for idx, action in enumerate(self.elementary_action_list):
write_message_to_log("Create IK constraints for action" + " " + str(idx) + " " + str(action.start_step) + " " + str(self.steps[action.start_step].start_frame), LOG_MODE_DEBUG)
if not self.constrain_place_orientation and action.action_name in self.place_action_list:
constrain_orientation = False
else:
constrain_orientation = True
start_step = action.start_step
end_step = action.end_step
elementary_action_ik_constraints = dict()
elementary_action_ik_constraints["keyframes"] = dict()
elementary_action_ik_constraints["trajectories"] = list()
elementary_action_ik_constraints["collision_avoidance"] = list()
frame_offset = self.steps[start_step].start_frame
for step in self.steps[start_step: end_step+1]:
time_function = None
if self.use_time_parameters and self.motion_state_graph.nodes[step.node_key].get_n_time_components() > 0:
time_function = self.motion_state_graph.nodes[step.node_key].back_project_time_function(step.parameters)
step_keyframe_constraints = step.motion_primitive_constraints.convert_to_ik_constraints(self.motion_state_graph, frame_offset, time_function, constrain_orientation)
elementary_action_ik_constraints["keyframes"].update(step_keyframe_constraints)
elementary_action_ik_constraints["collision_avoidance"] += step.motion_primitive_constraints.get_ca_constraints()
frame_offset += step.end_frame - step.start_frame + 1
if self._algorithm_config["collision_avoidance_constraints_mode"] == "ik":
elementary_action_ik_constraints["trajectories"] += self._create_ik_trajectory_constraints_from_ca_trajectories(idx)
elementary_action_ik_constraints["trajectories"] += self._create_ik_trajectory_constraints_from_annotated_trajectories(idx)
ik_constraints.append(elementary_action_ik_constraints)
return ik_constraints
def _create_ik_constraints2(self):
ik_constraints = collections.OrderedDict()
for idx, action in enumerate(self.elementary_action_list):
write_message_to_log("Create IK constraints for action" + " " + str(idx) + " " + str(action.start_step) + " " + str(self.steps[action.start_step].start_frame), LOG_MODE_DEBUG)
if not self.constrain_place_orientation and action.action_name in self.place_action_list:
constrain_orientation = False
else:
constrain_orientation = True
start_step = action.start_step
end_step = action.end_step
frame_offset = self.steps[start_step].start_frame
for step in self.steps[start_step: end_step + 1]:
time_function = None
if self.use_time_parameters and self.motion_state_graph.nodes[step.node_key].get_n_time_components() > 0:
time_function = self.motion_state_graph.nodes[step.node_key].back_project_time_function(step.parameters)
step_constraints = step.motion_primitive_constraints.convert_to_ik_constraints(
self.motion_state_graph, frame_offset, time_function, constrain_orientation, version=2)
ik_constraints.update(step_constraints)
frame_offset += step.end_frame - step.start_frame + 1
return ik_constraints
def _create_ik_trajectory_constraints_from_ca_trajectories(self, action_idx):
frame_annotation = self.keyframe_event_list.frame_annotation['elementaryActionSequence'][action_idx]
trajectory_constraints = list()
action = self.elementary_action_list[action_idx]
for ca_constraint in action.action_constraints.collision_avoidance_constraints:
traj_constraint = dict()
traj_constraint["trajectory"] = ca_constraint
traj_constraint["fixed_range"] = False # search for closer start
traj_constraint["constrain_orientation"] = False
traj_constraint["start_frame"] = frame_annotation["startFrame"]
traj_constraint["end_frame"] = frame_annotation["endFrame"]
#TODO find a better solution than this workaround that undoes the joint name mapping from hands to tool bones for ca constraints
if self.mg_input.activate_joint_mapping and ca_constraint.joint_name in list(self.mg_input.inverse_joint_name_map.keys()):
joint_name = self.mg_input.inverse_joint_name_map[ca_constraint.joint_name]
else:
joint_name = ca_constraint.joint_name
traj_constraint["joint_name"] = joint_name
traj_constraint["delta"] = 1.0
trajectory_constraints.append(traj_constraint)
return trajectory_constraints
def _create_ik_trajectory_constraints_from_annotated_trajectories(self, action_idx):
write_message_to_log("extract annotated trajectories", LOG_MODE_DEBUG)
frame_annotation = self.keyframe_event_list.frame_annotation['elementaryActionSequence'][action_idx]
start_frame = frame_annotation["startFrame"]
trajectory_constraints = list()
action = self.elementary_action_list[action_idx]
for constraint in action.action_constraints.annotated_trajectory_constraints:
label = list(constraint.semantic_annotation.keys())[0]
write_message_to_log("trajectory constraint label " + str(list(constraint.semantic_annotation.keys())), LOG_MODE_DEBUG)
action_name = action.action_name
for step in self.steps[action.start_step: action.end_step+1]:
motion_primitive_name = step.node_key[1]
write_message_to_log("look for action annotation of " + action_name+" "+motion_primitive_name, LOG_MODE_DEBUG)
if motion_primitive_name not in self.motion_state_graph.node_groups[action_name].motion_primitive_annotation_regions:
continue
annotations = self.motion_state_graph.node_groups[action_name].motion_primitive_annotation_regions[motion_primitive_name]
write_message_to_log("action annotation" + str(annotations) +" "+ str(frame_annotation["startFrame"]) + " " + str(frame_annotation["endFrame"]),
LOG_MODE_DEBUG)
if label not in list(annotations.keys()):
continue
annotation_range = annotations[label]
traj_constraint = dict()
traj_constraint["trajectory"] = constraint
traj_constraint["constrain_orientation"] = True
traj_constraint["fixed_range"] = True
time_function = None
if self.use_time_parameters and self.motion_state_graph.nodes[step.node_key].get_n_time_components() > 0:
time_function = self.motion_state_graph.nodes[step.node_key].back_project_time_function(step.parameters)
if time_function is None:
traj_constraint["start_frame"] = start_frame + annotation_range[0]
traj_constraint["end_frame"] = start_frame + annotation_range[1]
else:
#add +1 for correct mapping TODO verify for all cases
traj_constraint["start_frame"] = start_frame + int(time_function[annotation_range[0]]) + 1
traj_constraint["end_frame"] = start_frame + int(time_function[annotation_range[1]]) + 1
if self.mg_input.activate_joint_mapping and constraint.joint_name in list(self.mg_input.inverse_joint_name_map.keys()):
joint_name = self.mg_input.inverse_joint_name_map[constraint.joint_name]
else:
joint_name = constraint.joint_name
traj_constraint["joint_name"] = joint_name
traj_constraint["delta"] = 1.0
write_message_to_log( "create ik trajectory constraint from label " + str(label), LOG_MODE_DEBUG)
trajectory_constraints.append(traj_constraint)
return trajectory_constraints
def get_average_keyframe_constraint_error(self):
keyframe_constraint_errors = []
step_index = 0
prev_frames = None
for step_idx, step in enumerate(self.steps):
quat_frames = self.motion_state_graph.nodes[step.node_key].back_project(step.parameters, use_time_parameters=False).get_motion_vector()
skeleton = self.motion_vector.skeleton
aligned_frames = align_and_concatenate_frames(skeleton, skeleton.aligning_root_node, quat_frames, prev_frames,
self.motion_vector.start_pose, 0)
for c_idx, constraint in enumerate(step.motion_primitive_constraints.constraints):
if constraint.constraint_type in [SPATIAL_CONSTRAINT_TYPE_KEYFRAME_POSITION, SPATIAL_CONSTRAINT_TYPE_TWO_HAND_POSITION] and\
not "generated" in list(constraint.semantic_annotation.keys()):
error = constraint.evaluate_motion_sample(aligned_frames)
write_message_to_log("Error of Keyframe constraint " +str(step_idx) + "-" + str(c_idx) +": " +str(error), LOG_MODE_DEBUG)
keyframe_constraint_errors.append(error)
prev_frames = aligned_frames
step_index += 1
if len(keyframe_constraint_errors) > 0:
return np.average(keyframe_constraint_errors)
else:
return -1
def get_generated_constraints(self):
step_count = 0
generated_constraints = dict()
for step in self.steps:
key = str(step.node_key) + str(step_count)
generated_constraints[key] = []
for constraint in step.motion_primitive_constraints.constraints:
if constraint.is_generated():
generated_constraints[key].append(constraint.position)
step_count += 1
return generated_constraints
def get_average_error(self):
average_error = 0
for step in self.steps:
average_error += step.motion_primitive_constraints.min_error
if average_error > 0:
average_error /= len(self.steps)
return average_error
def get_number_of_object_evaluations(self):
objective_evaluations = 0
for step in self.steps:
objective_evaluations += step.motion_primitive_constraints.evaluations
return objective_evaluations
def print_statistics(self):
print(self.get_statistics_string())
def get_statistics_string(self):
average_error = self.get_average_error()
evaluations_string = "Total number of objective evaluations " + str(self.get_number_of_object_evaluations())
error_string = "Average error for " + str(len(self.steps)) + \
" motion primitives: " + str(average_error)
average_keyframe_error = self.get_average_keyframe_constraint_error()
if average_keyframe_error > -1:
average_keyframe_error_string = "Average keyframe constraint error " + str(average_keyframe_error)
else:
average_keyframe_error_string = "No keyframe constraint specified"
average_time_per_step = 0.0
for step in self.steps:
average_time_per_step += step.motion_primitive_constraints.time
average_time_per_step /= len(self.steps)
average_time_string = "Average time per motion primitive " + str(average_time_per_step)
return average_keyframe_error_string + "\n" + evaluations_string + "\n" + average_time_string + "\n" + error_string
def export_generated_constraints(self, file_path="goals.path"):
""" Converts constraints that were generated based on input constraints into a json dictionary for a debug visualization
"""
root_control_point_data = []
hand_constraint_data = []
for idx, step in enumerate(self.steps):
step_constraints = {"semanticAnnotation": {"step": idx}}
for c in step.motion_primitive_constraints.constraints:
if c.constraint_type == "keyframe_position" and c.joint_name == self.motion_state_graph.skeleton.root:
p = c.position
if p is not None:
step_constraints["position"] = [p[0], -p[2], None]
elif c.constraint_type == "keyframe_2d_direction":
step_constraints["direction"] = c.direction_constraint.tolist()
elif c.constraint_type == "ca_constraint":
#if c.constraint_type in ["RightHand", "LeftHand"]:
position = [c.position[0], -c.position[2], c.position[1]]
hand_constraint = {"position": position}
hand_constraint_data.append(hand_constraint)
root_control_point_data.append(step_constraints)
constraints = {"tasks": [{"elementaryActions":[{
"action": "walk",
"constraints": [{"joint": "Hips",
"keyframeConstraints": root_control_point_data },
{"joint": "RightHand",
"keyframeConstraints": hand_constraint_data}]
}]
}]
}
constraints["startPose"] = {"position":[0,0,0], "orientation": [0,0,0]}
constraints["session"] = "session"
with open(file_path, "wb") as out:
json.dump(constraints, out)
def get_number_of_actions(self):
return len(self.elementary_action_list)
def plot_constraints(self, file_name="traj"):
for idx, action in enumerate(self.elementary_action_list):
start_frame = self.steps[action.start_step].start_frame
end_frame = self.steps[action.end_step].end_frame
root_motion = self.motion_vector.frames#[start_frame:end_frame,:3]
if action.action_constraints.root_trajectory is not None:
traj_constraint = action.action_constraints.root_trajectory
plot_annotated_spline(traj_constraint,root_motion, file_name+str(idx)+".png")
def to_json(self):
data = dict()
data["algorithm_config"] = self._algorithm_config
data["start_pose"] = self.motion_vector.start_pose
data["steps"] = []
for step in self.steps:
data["steps"].append(step.to_json())
return data
@staticmethod
def from_json(graph, data):
graph_walk = GraphWalk(graph, None, data["algorithm_config"], data["start_pose"])
graph_walk.steps = []
for step_data in data["steps"]:
graph_walk.steps.append(GraphWalkEntry.from_json(graph, step_data))
return graph_walk
def save_to_file(self, file_path):
with open(file_path, "wb") as out:
json.dump(self.to_json(), out)
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25342,
1873,
1275,
29871,
29906,
29901,
13,
9651,
9732,
630,
29918,
29885,
8194,
29918,
8111,
29889,
638,
29918,
13646,
29879,
353,
1583,
3032,
3258,
29918,
638,
29918,
13646,
29879,
29906,
580,
13,
4706,
9732,
630,
29918,
29885,
8194,
29918,
8111,
29889,
4262,
29918,
20919,
353,
1583,
13,
4706,
736,
9732,
630,
29918,
29885,
8194,
29918,
8111,
13,
13,
1678,
822,
679,
29918,
2467,
29918,
3166,
29918,
1989,
2557,
29898,
1311,
29892,
1820,
2557,
1125,
13,
4706,
1476,
29918,
2467,
29918,
2248,
353,
448,
29896,
13,
4706,
4331,
29918,
2248,
353,
1583,
29889,
657,
29918,
10568,
29918,
3166,
29918,
1989,
2557,
29898,
1989,
2557,
29897,
13,
4706,
2436,
29918,
4906,
29918,
517,
29918,
1188,
703,
9692,
1820,
2557,
297,
4331,
376,
718,
851,
29898,
10568,
29918,
2248,
511,
25401,
29918,
20387,
29918,
18525,
29897,
13,
4706,
565,
4331,
29918,
2248,
529,
29871,
29900,
29901,
13,
9651,
736,
1476,
29918,
2467,
29918,
2248,
13,
4706,
363,
3158,
29918,
2248,
29892,
3158,
297,
26985,
29898,
1311,
29889,
5029,
653,
29918,
2467,
29918,
1761,
1125,
13,
9651,
565,
3158,
29889,
2962,
29918,
10568,
5277,
4331,
29918,
2248,
5277,
3158,
29889,
355,
29918,
10568,
29901,
13,
18884,
1476,
29918,
2467,
29918,
2248,
353,
3158,
29918,
2248,
13,
4706,
736,
1476,
29918,
2467,
29918,
2248,
13,
13,
1678,
822,
679,
29918,
10568,
29918,
3166,
29918,
1989,
2557,
29898,
1311,
29892,
1820,
2557,
1125,
13,
4706,
1476,
29918,
10568,
29918,
2248,
353,
448,
29896,
13,
4706,
363,
4331,
29918,
2248,
29892,
4331,
297,
26985,
29898,
1311,
29889,
24530,
1125,
13,
9651,
396,
9842,
278,
1369,
29918,
2557,
322,
1095,
29918,
2557,
526,
1370,
9795,
297,
2767,
29918,
7382,
29918,
29885,
8194,
29918,
8111,
13,
9651,
396,
2158,
4331,
29889,
2962,
29918,
2557,
29892,
1820,
2557,
29892,
4331,
29889,
355,
29918,
2557,
13,
9651,
565,
4331,
29889,
2962,
29918,
2557,
5277,
1820,
2557,
5277,
4331,
29889,
355,
29918,
2557,
29901,
13,
18884,
1476,
29918,
10568,
29918,
2248,
353,
4331,
29918,
2248,
13,
4706,
736,
1476,
29918,
10568,
29918,
2248,
13,
13,
1678,
822,
3588,
29918,
4262,
29918,
20919,
29918,
517,
29918,
339,
25744,
291,
29918,
19935,
29898,
1311,
29892,
1369,
29918,
10568,
29922,
29900,
29892,
671,
29918,
2230,
29918,
16744,
29922,
8824,
29892,
4331,
29918,
2311,
29922,
29896,
29889,
29900,
1125,
13,
4706,
9995,
13,
4706,
584,
3207,
1369,
29918,
10568,
29901,
13,
4706,
584,
2457,
29901,
13,
4706,
9995,
13,
4706,
565,
1369,
29918,
10568,
1275,
29871,
29900,
29901,
13,
9651,
1369,
29918,
2557,
353,
29871,
29900,
13,
4706,
1683,
29901,
13,
9651,
1369,
29918,
2557,
353,
1583,
29889,
24530,
29961,
2962,
29918,
10568,
1822,
2962,
29918,
2557,
13,
4706,
1583,
29889,
29885,
8194,
29918,
8111,
29889,
8551,
29898,
355,
29918,
2557,
29922,
2962,
29918,
2557,
29897,
13,
4706,
363,
4331,
297,
1583,
29889,
24530,
29961,
2962,
29918,
10568,
29901,
5387,
13,
9651,
4331,
29889,
2962,
29918,
2557,
353,
1369,
29918,
2557,
13,
9651,
396,
3539,
29918,
1188,
29898,
10568,
29889,
3177,
29918,
1989,
29892,
7431,
29898,
10568,
29889,
16744,
876,
13,
9651,
439,
271,
29918,
19935,
353,
1583,
29889,
29885,
8194,
29918,
3859,
29918,
4262,
29889,
18010,
29961,
10568,
29889,
3177,
29918,
1989,
1822,
1627,
29918,
4836,
29898,
10568,
29889,
16744,
29892,
671,
29918,
2230,
29918,
16744,
29892,
4331,
29918,
2311,
467,
657,
29918,
29885,
8194,
29918,
8111,
580,
13,
9651,
565,
4331,
29889,
3177,
29918,
1989,
29961,
29896,
1822,
13609,
2141,
1975,
2541,
703,
1563,
303,
749,
29908,
1125,
13,
18884,
3661,
29918,
12090,
353,
376,
6661,
29918,
29878,
29908,
13,
9651,
25342,
4331,
29889,
3177,
29918,
1989,
29961,
29896,
1822,
13609,
2141,
1975,
2541,
703,
1266,
303,
749,
29908,
1125,
13,
18884,
3661,
29918,
12090,
353,
376,
6661,
29918,
29880,
29908,
13,
9651,
1683,
29901,
13,
18884,
3661,
29918,
12090,
353,
6213,
13,
9651,
1583,
29889,
29885,
8194,
29918,
8111,
29889,
4397,
29918,
19935,
29898,
339,
271,
29918,
19935,
29892,
3661,
29918,
12090,
29897,
13,
9651,
4331,
29889,
355,
29918,
2557,
353,
1583,
29889,
657,
29918,
1949,
29918,
974,
29918,
19935,
580,
29899,
29896,
13,
9651,
1369,
29918,
2557,
353,
4331,
29889,
355,
29918,
2557,
718,
29871,
29896,
13,
13,
1678,
822,
679,
29918,
10945,
29918,
1028,
15238,
29918,
15501,
29918,
8111,
29898,
1311,
29892,
1369,
29918,
10568,
29922,
29900,
1125,
13,
4706,
2847,
29918,
2543,
404,
353,
5159,
13,
4706,
363,
4331,
297,
1583,
29889,
24530,
29961,
2962,
29918,
10568,
29901,
5387,
13,
9651,
2847,
29918,
2543,
404,
4619,
4331,
29889,
16744,
7503,
10568,
29889,
29876,
29918,
1028,
15238,
29918,
14036,
1822,
25027,
391,
580,
13,
4706,
736,
2847,
29918,
2543,
404,
13,
13,
1678,
822,
679,
29918,
10945,
29918,
2230,
29918,
15501,
29918,
8111,
29898,
1311,
29892,
1369,
29918,
10568,
29922,
29900,
1125,
13,
4706,
2847,
29918,
2543,
404,
353,
5159,
13,
4706,
363,
4331,
297,
1583,
29889,
24530,
29961,
2962,
29918,
10568,
29901,
5387,
13,
9651,
2847,
29918,
2543,
404,
4619,
4331,
29889,
16744,
29961,
10568,
29889,
29876,
29918,
1028,
15238,
29918,
14036,
29901,
1822,
25027,
391,
580,
13,
4706,
736,
2847,
29918,
2543,
404,
13,
13,
1678,
822,
2767,
29918,
1028,
15238,
29918,
16744,
29898,
1311,
29892,
3443,
29918,
8111,
29892,
1369,
29918,
10568,
29922,
29900,
1125,
13,
4706,
2436,
29918,
4906,
29918,
517,
29918,
1188,
703,
6422,
18652,
4128,
613,
25401,
29918,
20387,
29918,
18525,
29897,
13,
4706,
9210,
353,
29871,
29900,
13,
4706,
363,
4331,
297,
1583,
29889,
24530,
29961,
2962,
29918,
10568,
29901,
5387,
13,
9651,
716,
29918,
2312,
353,
3443,
29918,
8111,
29961,
10289,
29901,
10289,
29974,
10568,
29889,
29876,
29918,
1028,
15238,
29918,
14036,
29962,
13,
9651,
4331,
29889,
16744,
7503,
10568,
29889,
29876,
29918,
1028,
15238,
29918,
14036,
29962,
353,
716,
29918,
2312,
13,
9651,
9210,
4619,
4331,
29889,
29876,
29918,
1028,
15238,
29918,
14036,
13,
13,
1678,
822,
2767,
29918,
2230,
29918,
16744,
29898,
1311,
29892,
3443,
29918,
8111,
29892,
1369,
29918,
10568,
29892,
1095,
29918,
10568,
1125,
13,
4706,
9210,
353,
29871,
29900,
13,
4706,
363,
4331,
297,
1583,
29889,
24530,
29961,
2962,
29918,
10568,
29901,
355,
29918,
10568,
5387,
13,
9651,
716,
29918,
4283,
353,
3443,
29918,
8111,
29961,
10289,
29901,
10289,
29974,
10568,
29889,
29876,
29918,
2230,
29918,
14036,
29962,
13,
9651,
4331,
29889,
16744,
29961,
10568,
29889,
29876,
29918,
1028,
15238,
29918,
14036,
17531,
353,
716,
29918,
4283,
13,
9651,
9210,
4619,
4331,
29889,
29876,
29918,
2230,
29918,
14036,
13,
13,
1678,
822,
9773,
29918,
339,
271,
29918,
19935,
29898,
1311,
29892,
716,
29918,
19935,
1125,
13,
4706,
1583,
29889,
29885,
8194,
29918,
8111,
29889,
4397,
29918,
19935,
29898,
1482,
29918,
19935,
29897,
13,
13,
1678,
822,
679,
29918,
339,
271,
29918,
19935,
29898,
1311,
1125,
13,
4706,
736,
1583,
29889,
29885,
8194,
29918,
8111,
29889,
19935,
13,
13,
1678,
822,
679,
29918,
1949,
29918,
974,
29918,
19935,
29898,
1311,
1125,
13,
4706,
736,
1583,
29889,
29885,
8194,
29918,
8111,
29889,
29876,
29918,
19935,
13,
13,
1678,
822,
2767,
29918,
2557,
29918,
18317,
29898,
1311,
29892,
3158,
29918,
978,
29892,
1369,
29918,
2557,
29892,
1095,
29918,
2557,
1125,
13,
4706,
9995,
3462,
29879,
263,
8600,
304,
1583,
29889,
2557,
29918,
18317,
2791,
292,
1369,
322,
1095,
13,
9651,
3515,
310,
385,
3158,
29889,
13,
4706,
9995,
13,
4706,
1583,
29889,
1989,
2557,
29918,
3696,
29918,
1761,
29889,
5504,
29918,
2557,
29918,
18317,
29898,
2467,
29918,
978,
29892,
1369,
29918,
2557,
29892,
1095,
29918,
2557,
29897,
13,
13,
1678,
822,
903,
3258,
29918,
638,
29918,
13646,
29879,
29898,
1311,
1125,
13,
4706,
12380,
29918,
13646,
29879,
353,
5159,
13,
4706,
363,
22645,
29892,
3158,
297,
26985,
29898,
1311,
29889,
5029,
653,
29918,
2467,
29918,
1761,
1125,
13,
9651,
2436,
29918,
4906,
29918,
517,
29918,
1188,
703,
4391,
306,
29968,
11938,
363,
3158,
29908,
718,
376,
376,
718,
851,
29898,
13140,
29897,
718,
376,
376,
718,
851,
29898,
2467,
29889,
2962,
29918,
10568,
29897,
718,
376,
376,
718,
851,
29898,
1311,
29889,
24530,
29961,
2467,
29889,
2962,
29918,
10568,
1822,
2962,
29918,
2557,
511,
25401,
29918,
20387,
29918,
18525,
29897,
13,
9651,
565,
451,
1583,
29889,
3075,
6038,
29918,
6689,
29918,
20659,
322,
3158,
29889,
2467,
29918,
978,
297,
1583,
29889,
6689,
29918,
2467,
29918,
1761,
29901,
13,
18884,
1040,
6038,
29918,
20659,
353,
7700,
13,
9651,
1683,
29901,
13,
18884,
1040,
6038,
29918,
20659,
353,
5852,
13,
9651,
1369,
29918,
10568,
353,
3158,
29889,
2962,
29918,
10568,
13,
9651,
1095,
29918,
10568,
353,
3158,
29889,
355,
29918,
10568,
13,
9651,
22470,
29918,
2467,
29918,
638,
29918,
13646,
29879,
353,
9657,
580,
13,
9651,
22470,
29918,
2467,
29918,
638,
29918,
13646,
29879,
3366,
1989,
19935,
3108,
353,
9657,
580,
13,
9651,
22470,
29918,
2467,
29918,
638,
29918,
13646,
29879,
3366,
3018,
622,
3842,
3108,
353,
1051,
580,
13,
9651,
22470,
29918,
2467,
29918,
638,
29918,
13646,
29879,
3366,
22017,
2459,
29918,
485,
3398,
749,
3108,
353,
1051,
580,
13,
9651,
3515,
29918,
10289,
353,
1583,
29889,
24530,
29961,
2962,
29918,
10568,
1822,
2962,
29918,
2557,
13,
9651,
363,
4331,
297,
1583,
29889,
24530,
29961,
2962,
29918,
10568,
29901,
1095,
29918,
10568,
29974,
29896,
5387,
13,
18884,
931,
29918,
2220,
353,
6213,
13,
18884,
565,
1583,
29889,
1509,
29918,
2230,
29918,
16744,
322,
1583,
29889,
29885,
8194,
29918,
3859,
29918,
4262,
29889,
18010,
29961,
10568,
29889,
3177,
29918,
1989,
1822,
657,
29918,
29876,
29918,
2230,
29918,
14036,
580,
1405,
29871,
29900,
29901,
13,
462,
1678,
931,
29918,
2220,
353,
1583,
29889,
29885,
8194,
29918,
3859,
29918,
4262,
29889,
18010,
29961,
10568,
29889,
3177,
29918,
1989,
1822,
1627,
29918,
4836,
29918,
2230,
29918,
2220,
29898,
10568,
29889,
16744,
29897,
13,
13,
18884,
4331,
29918,
1989,
2557,
29918,
13646,
29879,
353,
4331,
29889,
29885,
8194,
29918,
9469,
3321,
29918,
13646,
29879,
29889,
13441,
29918,
517,
29918,
638,
29918,
13646,
29879,
29898,
1311,
29889,
29885,
8194,
29918,
3859,
29918,
4262,
29892,
3515,
29918,
10289,
29892,
931,
29918,
2220,
29892,
1040,
6038,
29918,
20659,
29897,
13,
13,
18884,
22470,
29918,
2467,
29918,
638,
29918,
13646,
29879,
3366,
1989,
19935,
16862,
5504,
29898,
10568,
29918,
1989,
2557,
29918,
13646,
29879,
29897,
13,
18884,
22470,
29918,
2467,
29918,
638,
29918,
13646,
29879,
3366,
22017,
2459,
29918,
485,
3398,
749,
3108,
4619,
4331,
29889,
29885,
8194,
29918,
9469,
3321,
29918,
13646,
29879,
29889,
657,
29918,
1113,
29918,
13646,
29879,
580,
13,
13,
18884,
3515,
29918,
10289,
4619,
4331,
29889,
355,
29918,
2557,
448,
4331,
29889,
2962,
29918,
2557,
718,
29871,
29896,
13,
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1678,
14002,
29918,
978,
353,
7276,
29889,
12090,
29918,
978,
13,
13,
18884,
1020,
29926,
29918,
13646,
3366,
12090,
29918,
978,
3108,
353,
14002,
29918,
978,
13,
18884,
1020,
29926,
29918,
13646,
3366,
4181,
3108,
353,
29871,
29896,
29889,
29900,
13,
18884,
2436,
29918,
4906,
29918,
517,
29918,
1188,
29898,
376,
3258,
12380,
23324,
706,
7276,
515,
3858,
376,
718,
851,
29898,
1643,
511,
25401,
29918,
20387,
29918,
18525,
29897,
13,
18884,
23324,
706,
29918,
13646,
29879,
29889,
4397,
29898,
3018,
29926,
29918,
13646,
29897,
13,
4706,
736,
23324,
706,
29918,
13646,
29879,
13,
13,
1678,
822,
679,
29918,
12483,
482,
29918,
1989,
2557,
29918,
13646,
29918,
2704,
29898,
1311,
1125,
13,
4706,
1820,
2557,
29918,
13646,
29918,
12523,
353,
5159,
13,
4706,
4331,
29918,
2248,
353,
29871,
29900,
13,
4706,
12379,
29918,
19935,
353,
6213,
13,
4706,
363,
4331,
29918,
13140,
29892,
4331,
297,
26985,
29898,
1311,
29889,
24530,
1125,
13,
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439,
271,
29918,
19935,
353,
1583,
29889,
29885,
8194,
29918,
3859,
29918,
4262,
29889,
18010,
29961,
10568,
29889,
3177,
29918,
1989,
1822,
1627,
29918,
4836,
29898,
10568,
29889,
16744,
29892,
671,
29918,
2230,
29918,
16744,
29922,
8824,
467,
657,
29918,
29885,
8194,
29918,
8111,
580,
13,
9651,
18109,
11285,
353,
1583,
29889,
29885,
8194,
29918,
8111,
29889,
26050,
11285,
13,
9651,
26118,
29918,
19935,
353,
7595,
29918,
392,
29918,
535,
29883,
2579,
403,
29918,
19935,
29898,
26050,
11285,
29892,
18109,
11285,
29889,
2520,
292,
29918,
4632,
29918,
3177,
29892,
439,
271,
29918,
19935,
29892,
12379,
29918,
19935,
29892,
13,
462,
462,
308,
1583,
29889,
29885,
8194,
29918,
8111,
29889,
2962,
29918,
4220,
29892,
29871,
29900,
29897,
13,
9651,
363,
274,
29918,
13140,
29892,
7276,
297,
26985,
29898,
10568,
29889,
29885,
8194,
29918,
9469,
3321,
29918,
13646,
29879,
29889,
13646,
29879,
1125,
13,
18884,
565,
7276,
29889,
13646,
29918,
1853,
297,
518,
5550,
1299,
25758,
29918,
6007,
1254,
4717,
10192,
29918,
11116,
29918,
10818,
29943,
4717,
2303,
29918,
24815,
22122,
29892,
10937,
1299,
25758,
29918,
6007,
1254,
4717,
10192,
29918,
11116,
29918,
16240,
29949,
29918,
29950,
9468,
29918,
24815,
22122,
29962,
322,
29905,
13,
462,
259,
451,
376,
13525,
29908,
297,
1051,
29898,
13646,
29889,
12846,
7716,
29918,
18317,
29889,
8149,
580,
1125,
13,
462,
1678,
1059,
353,
7276,
29889,
24219,
403,
29918,
29885,
8194,
29918,
11249,
29898,
13671,
29918,
19935,
29897,
13,
462,
1678,
2436,
29918,
4906,
29918,
517,
29918,
1188,
703,
2392,
310,
7670,
2557,
7276,
376,
718,
710,
29898,
10568,
29918,
13140,
29897,
718,
11663,
29908,
718,
851,
29898,
29883,
29918,
13140,
29897,
718,
1115,
376,
718,
710,
29898,
2704,
511,
25401,
29918,
20387,
29918,
18525,
29897,
13,
462,
1678,
1820,
2557,
29918,
13646,
29918,
12523,
29889,
4397,
29898,
2704,
29897,
13,
9651,
12379,
29918,
19935,
353,
26118,
29918,
19935,
13,
9651,
4331,
29918,
2248,
4619,
29871,
29896,
13,
4706,
565,
7431,
29898,
1989,
2557,
29918,
13646,
29918,
12523,
29897,
1405,
29871,
29900,
29901,
13,
9651,
736,
7442,
29889,
12483,
482,
29898,
1989,
2557,
29918,
13646,
29918,
12523,
29897,
13,
4706,
1683,
29901,
13,
9651,
736,
448,
29896,
13,
13,
1678,
822,
679,
29918,
13525,
29918,
13646,
29879,
29898,
1311,
1125,
13,
4706,
4331,
29918,
2798,
353,
29871,
29900,
13,
4706,
5759,
29918,
13646,
29879,
353,
9657,
580,
13,
4706,
363,
4331,
297,
1583,
29889,
24530,
29901,
13,
9651,
1820,
353,
851,
29898,
10568,
29889,
3177,
29918,
1989,
29897,
718,
851,
29898,
10568,
29918,
2798,
29897,
13,
9651,
5759,
29918,
13646,
29879,
29961,
1989,
29962,
353,
5159,
13,
9651,
363,
7276,
297,
4331,
29889,
29885,
8194,
29918,
9469,
3321,
29918,
13646,
29879,
29889,
13646,
29879,
29901,
13,
18884,
565,
7276,
29889,
275,
29918,
13525,
7295,
13,
462,
1678,
5759,
29918,
13646,
29879,
29961,
1989,
1822,
4397,
29898,
13646,
29889,
3283,
29897,
13,
9651,
4331,
29918,
2798,
4619,
29871,
29896,
13,
4706,
736,
5759,
29918,
13646,
29879,
13,
13,
1678,
822,
679,
29918,
12483,
482,
29918,
2704,
29898,
1311,
1125,
13,
4706,
6588,
29918,
2704,
353,
29871,
29900,
13,
4706,
363,
4331,
297,
1583,
29889,
24530,
29901,
13,
9651,
6588,
29918,
2704,
4619,
4331,
29889,
29885,
8194,
29918,
9469,
3321,
29918,
13646,
29879,
29889,
1195,
29918,
2704,
13,
4706,
565,
6588,
29918,
2704,
1405,
29871,
29900,
29901,
13,
9651,
6588,
29918,
2704,
847,
29922,
7431,
29898,
1311,
29889,
24530,
29897,
13,
4706,
736,
6588,
29918,
2704,
13,
13,
1678,
822,
679,
29918,
4537,
29918,
974,
29918,
3318,
29918,
24219,
800,
29898,
1311,
1125,
13,
4706,
12091,
29918,
24219,
800,
353,
29871,
29900,
13,
4706,
363,
4331,
297,
1583,
29889,
24530,
29901,
13,
9651,
12091,
29918,
24219,
800,
4619,
4331,
29889,
29885,
8194,
29918,
9469,
3321,
29918,
13646,
29879,
29889,
24219,
800,
13,
4706,
736,
12091,
29918,
24219,
800,
13,
13,
1678,
822,
1596,
29918,
6112,
6765,
29898,
1311,
1125,
13,
4706,
1596,
29898,
1311,
29889,
657,
29918,
6112,
6765,
29918,
1807,
3101,
13,
13,
1678,
822,
679,
29918,
6112,
6765,
29918,
1807,
29898,
1311,
1125,
13,
4706,
6588,
29918,
2704,
353,
1583,
29889,
657,
29918,
12483,
482,
29918,
2704,
580,
13,
4706,
6161,
800,
29918,
1807,
353,
376,
11536,
1353,
310,
12091,
6161,
800,
376,
718,
851,
29898,
1311,
29889,
657,
29918,
4537,
29918,
974,
29918,
3318,
29918,
24219,
800,
3101,
13,
4706,
1059,
29918,
1807,
353,
376,
29909,
19698,
1059,
363,
376,
718,
851,
29898,
2435,
29898,
1311,
29889,
24530,
876,
718,
320,
13,
462,
539,
376,
10884,
28147,
3145,
29901,
376,
718,
851,
29898,
12483,
482,
29918,
2704,
29897,
13,
4706,
6588,
29918,
1989,
2557,
29918,
2704,
353,
1583,
29889,
657,
29918,
12483,
482,
29918,
1989,
2557,
29918,
13646,
29918,
2704,
580,
13,
4706,
565,
6588,
29918,
1989,
2557,
29918,
2704,
1405,
448,
29896,
29901,
13,
9651,
6588,
29918,
1989,
2557,
29918,
2704,
29918,
1807,
353,
376,
29909,
19698,
1820,
2557,
7276,
1059,
376,
718,
851,
29898,
12483,
482,
29918,
1989,
2557,
29918,
2704,
29897,
13,
4706,
1683,
29901,
13,
9651,
6588,
29918,
1989,
2557,
29918,
2704,
29918,
1807,
353,
376,
3782,
1820,
2557,
7276,
6790,
29908,
13,
4706,
6588,
29918,
2230,
29918,
546,
29918,
10568,
353,
29871,
29900,
29889,
29900,
13,
4706,
363,
4331,
297,
1583,
29889,
24530,
29901,
13,
9651,
6588,
29918,
2230,
29918,
546,
29918,
10568,
4619,
4331,
29889,
29885,
8194,
29918,
9469,
3321,
29918,
13646,
29879,
29889,
2230,
13,
4706,
6588,
29918,
2230,
29918,
546,
29918,
10568,
847,
29922,
7431,
29898,
1311,
29889,
24530,
29897,
13,
4706,
6588,
29918,
2230,
29918,
1807,
353,
376,
29909,
19698,
931,
639,
10884,
19269,
376,
718,
851,
29898,
12483,
482,
29918,
2230,
29918,
546,
29918,
10568,
29897,
13,
4706,
736,
6588,
29918,
1989,
2557,
29918,
2704,
29918,
1807,
718,
6634,
29876,
29908,
718,
6161,
800,
29918,
1807,
718,
6634,
29876,
29908,
718,
6588,
29918,
2230,
29918,
1807,
718,
6634,
29876,
29908,
718,
1059,
29918,
1807,
13,
13,
1678,
822,
5609,
29918,
13525,
29918,
13646,
29879,
29898,
1311,
29892,
934,
29918,
2084,
543,
1484,
1338,
29889,
2084,
29908,
1125,
13,
4706,
9995,
1281,
369,
1372,
11938,
393,
892,
5759,
2729,
373,
1881,
11938,
964,
263,
4390,
8600,
363,
263,
4744,
7604,
2133,
13,
4706,
9995,
13,
4706,
3876,
29918,
6451,
29918,
3149,
29918,
1272,
353,
5159,
13,
4706,
1361,
29918,
13646,
29918,
1272,
353,
5159,
13,
4706,
363,
22645,
29892,
4331,
297,
26985,
29898,
1311,
29889,
24530,
1125,
13,
9651,
4331,
29918,
13646,
29879,
353,
8853,
12846,
7716,
21978,
1115,
8853,
10568,
1115,
22645,
930,
13,
9651,
363,
274,
297,
4331,
29889,
29885,
8194,
29918,
9469,
3321,
29918,
13646,
29879,
29889,
13646,
29879,
29901,
13,
18884,
565,
274,
29889,
13646,
29918,
1853,
1275,
376,
1989,
2557,
29918,
3283,
29908,
322,
274,
29889,
12090,
29918,
978,
1275,
1583,
29889,
29885,
8194,
29918,
3859,
29918,
4262,
29889,
26050,
11285,
29889,
4632,
29901,
13,
462,
1678,
282,
353,
274,
29889,
3283,
13,
462,
1678,
565,
282,
338,
451,
6213,
29901,
13,
462,
4706,
4331,
29918,
13646,
29879,
3366,
3283,
3108,
353,
518,
29886,
29961,
29900,
1402,
448,
29886,
29961,
29906,
1402,
6213,
29962,
13,
18884,
25342,
274,
29889,
13646,
29918,
1853,
1275,
376,
1989,
2557,
29918,
29906,
29881,
29918,
20845,
1115,
13,
462,
4706,
4331,
29918,
13646,
29879,
3366,
20845,
3108,
353,
274,
29889,
20845,
29918,
13646,
29889,
25027,
391,
580,
13,
18884,
25342,
274,
29889,
13646,
29918,
1853,
1275,
376,
1113,
29918,
13646,
1115,
13,
462,
1678,
396,
361,
274,
29889,
13646,
29918,
1853,
297,
6796,
7341,
3481,
613,
376,
8091,
3481,
3108,
29901,
13,
462,
1678,
2602,
353,
518,
29883,
29889,
3283,
29961,
29900,
1402,
448,
29883,
29889,
3283,
29961,
29906,
1402,
274,
29889,
3283,
29961,
29896,
5262,
13,
462,
1678,
1361,
29918,
13646,
353,
8853,
3283,
1115,
2602,
29913,
13,
462,
1678,
1361,
29918,
13646,
29918,
1272,
29889,
4397,
29898,
3179,
29918,
13646,
29897,
13,
9651,
3876,
29918,
6451,
29918,
3149,
29918,
1272,
29889,
4397,
29898,
10568,
29918,
13646,
29879,
29897,
13,
13,
13,
4706,
11938,
353,
8853,
20673,
1115,
518,
6377,
5029,
653,
26525,
1115,
19660,
13,
462,
462,
462,
418,
376,
2467,
1115,
376,
20919,
613,
13,
462,
462,
462,
418,
376,
13646,
29879,
1115,
518,
6377,
12090,
1115,
376,
29950,
4512,
613,
13,
462,
462,
462,
462,
539,
376,
1989,
2557,
27427,
1115,
3876,
29918,
6451,
29918,
3149,
29918,
1272,
29871,
2981,
13,
462,
462,
462,
462,
418,
8853,
12090,
1115,
376,
7341,
3481,
613,
13,
462,
462,
462,
462,
539,
376,
1989,
2557,
27427,
1115,
1361,
29918,
13646,
29918,
1272,
6525,
13,
462,
462,
462,
418,
500,
29962,
13,
462,
462,
500,
29962,
13,
462,
539,
500,
13,
13,
4706,
11938,
3366,
2962,
29925,
852,
3108,
353,
8853,
3283,
1115,
29961,
29900,
29892,
29900,
29892,
29900,
1402,
376,
20659,
1115,
518,
29900,
29892,
29900,
29892,
29900,
12258,
13,
4706,
11938,
3366,
7924,
3108,
353,
376,
7924,
29908,
13,
4706,
411,
1722,
29898,
1445,
29918,
2084,
29892,
376,
29893,
29890,
1159,
408,
714,
29901,
13,
9651,
4390,
29889,
15070,
29898,
13646,
29879,
29892,
714,
29897,
13,
13,
1678,
822,
679,
29918,
4537,
29918,
974,
29918,
7387,
29898,
1311,
1125,
13,
4706,
736,
7431,
29898,
1311,
29889,
5029,
653,
29918,
2467,
29918,
1761,
29897,
13,
13,
1678,
822,
6492,
29918,
13646,
29879,
29898,
1311,
29892,
934,
29918,
978,
543,
3018,
29926,
29908,
1125,
13,
4706,
363,
22645,
29892,
3158,
297,
26985,
29898,
1311,
29889,
5029,
653,
29918,
2467,
29918,
1761,
1125,
13,
9651,
1369,
29918,
2557,
353,
1583,
29889,
24530,
29961,
2467,
29889,
2962,
29918,
10568,
1822,
2962,
29918,
2557,
13,
9651,
1095,
29918,
2557,
353,
1583,
29889,
24530,
29961,
2467,
29889,
355,
29918,
10568,
1822,
355,
29918,
2557,
13,
13,
9651,
3876,
29918,
29885,
8194,
353,
1583,
29889,
29885,
8194,
29918,
8111,
29889,
19935,
29937,
29961,
2962,
29918,
2557,
29901,
355,
29918,
2557,
29892,
29901,
29941,
29962,
13,
9651,
565,
3158,
29889,
2467,
29918,
13646,
29879,
29889,
4632,
29918,
3018,
622,
706,
338,
451,
6213,
29901,
13,
18884,
1020,
29926,
29918,
13646,
353,
3158,
29889,
2467,
29918,
13646,
29879,
29889,
4632,
29918,
3018,
622,
706,
13,
18884,
6492,
29918,
6735,
630,
29918,
23579,
457,
29898,
3018,
29926,
29918,
13646,
29892,
4632,
29918,
29885,
8194,
29892,
934,
29918,
978,
29974,
710,
29898,
13140,
7240,
1642,
2732,
1159,
13,
13,
1678,
822,
304,
29918,
3126,
29898,
1311,
1125,
13,
4706,
848,
353,
9657,
580,
13,
4706,
848,
3366,
20567,
29918,
2917,
3108,
353,
1583,
3032,
20567,
29918,
2917,
13,
4706,
848,
3366,
2962,
29918,
4220,
3108,
353,
1583,
29889,
29885,
8194,
29918,
8111,
29889,
2962,
29918,
4220,
13,
4706,
848,
3366,
24530,
3108,
353,
5159,
13,
4706,
363,
4331,
297,
1583,
29889,
24530,
29901,
13,
9651,
848,
3366,
24530,
16862,
4397,
29898,
10568,
29889,
517,
29918,
3126,
3101,
13,
4706,
736,
848,
13,
13,
1678,
732,
7959,
5696,
13,
1678,
822,
515,
29918,
3126,
29898,
4262,
29892,
848,
1125,
13,
4706,
3983,
29918,
20919,
353,
12367,
29956,
2235,
29898,
4262,
29892,
6213,
29892,
848,
3366,
20567,
29918,
2917,
12436,
848,
3366,
2962,
29918,
4220,
20068,
13,
4706,
3983,
29918,
20919,
29889,
24530,
353,
5159,
13,
4706,
363,
4331,
29918,
1272,
297,
848,
3366,
24530,
3108,
29901,
13,
9651,
3983,
29918,
20919,
29889,
24530,
29889,
4397,
29898,
9527,
29956,
2235,
9634,
29889,
3166,
29918,
3126,
29898,
4262,
29892,
4331,
29918,
1272,
876,
13,
4706,
736,
3983,
29918,
20919,
13,
13,
1678,
822,
4078,
29918,
517,
29918,
1445,
29898,
1311,
29892,
934,
29918,
2084,
1125,
13,
4706,
411,
1722,
29898,
1445,
29918,
2084,
29892,
376,
29893,
29890,
1159,
408,
714,
29901,
13,
9651,
4390,
29889,
15070,
29898,
1311,
29889,
517,
29918,
3126,
3285,
714,
29897,
13,
2
] |
dime/__init__.py
|
CURENT/dime
| 0 |
149009
|
from .dime import Dime
|
[
1,
515,
869,
29881,
603,
1053,
360,
603,
13,
2
] |
problem01.py
|
goznauk/ProjectEuler.py
| 0 |
50161
|
<reponame>goznauk/ProjectEuler.py<gh_stars>0
i = 1
sum = 0
for i in range(1000) :
if i%3==0 :
sum += i
elif i%5==0 :
sum += i
print sum
|
[
1,
529,
276,
1112,
420,
29958,
1484,
3749,
585,
29895,
29914,
7653,
29923,
8584,
29889,
2272,
29966,
12443,
29918,
303,
1503,
29958,
29900,
13,
13,
29875,
353,
29871,
29896,
13,
2083,
353,
29871,
29900,
13,
1454,
474,
297,
3464,
29898,
29896,
29900,
29900,
29900,
29897,
584,
13,
12,
361,
474,
29995,
29941,
1360,
29900,
584,
13,
12,
12,
2083,
4619,
474,
13,
12,
23681,
474,
29995,
29945,
1360,
29900,
584,
13,
12,
12,
2083,
4619,
474,
13,
13,
13,
2158,
2533,
13,
2
] |
main.py
|
Alyxion/AquasuiteTempSync
| 0 |
165592
|
<gh_stars>0
# Simple Python script which fetches JSON data in the Circonus from the Aquasuite Web Service
# and stores it in a local file so it can be progressed by the FanControl tool by <NAME>
import requests
import os
import json
import time
import signal
import traceback
class GracefulKiller:
kill_now = False
def __init__(self):
signal.signal(signal.SIGINT, self.exit_gracefully)
signal.signal(signal.SIGTERM, self.exit_gracefully)
def exit_gracefully(self, signum, frame):
self.kill_now = True
if __name__ == "__main__":
target_filename = os.environ["FAN_CONTROL_TARGET_SENSOR"]
temperature_url = os.environ["TEMPERATURE_URL"]
update_frequency = float(os.environ["UPDATE_FREQUENCY_SECONDS"])
print(f"Synching sensor data from {temperature_url} to {target_filename}")
print(f"Temperature update frequency: {update_frequency:0.1f}s")
gc = GracefulKiller()
while not gc.kill_now:
try:
data_value = 0.0
# Fetch data from Aquasuite server
try:
response = requests.get(temperature_url, timeout=5)
temp_data = json.loads(response.text)
data_value = list(temp_data.values())[0]
except:
print(f"An error occurred while trying to fetch temperature update. {traceback.format_exc()}")
temperature = "0.0"
# Try to process data
try:
temperature: str = data_value
float_temperature = float(temperature) # cast to float. if this fails (None) we won't write it to disk
except:
print(f"An error occurred while trying to fetch temperature update. {traceback.format_exc()}")
temperature = "20.0"
# Store value in fan control file
with open(f"/fancontrol/{target_filename}", "w") as temperature_file:
print(f"Received water temperature update: {temperature}")
temperature = temperature.replace(".", ",")
temperature_file.write(temperature)
except:
print(f"An error occurred while trying to store temperature update. {traceback.format_exc()}")
pass
for counter in range(0, int(update_frequency)):
time.sleep(1.0)
if gc.kill_now:
break
|
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1,
529,
12443,
29918,
303,
1503,
29958,
29900,
13,
29937,
12545,
5132,
2471,
607,
6699,
267,
4663,
848,
297,
278,
25079,
535,
375,
515,
278,
16020,
294,
29884,
568,
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6692,
13,
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322,
14422,
372,
297,
263,
1887,
934,
577,
372,
508,
367,
6728,
287,
491,
278,
383,
273,
4809,
5780,
491,
529,
5813,
29958,
13,
13,
5215,
7274,
13,
5215,
2897,
13,
5215,
4390,
13,
5215,
931,
13,
5215,
7182,
13,
5215,
9637,
1627,
13,
13,
1990,
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1319,
29968,
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353,
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3874,
346,
3730,
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25436,
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546,
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29901,
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13,
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29918,
1445,
29889,
3539,
29898,
12863,
1535,
29897,
13,
4706,
5174,
29901,
13,
9651,
1596,
29898,
29888,
29908,
2744,
1059,
10761,
1550,
1811,
304,
3787,
10430,
2767,
29889,
426,
15003,
1627,
29889,
4830,
29918,
735,
29883,
580,
27195,
13,
9651,
1209,
13,
4706,
363,
6795,
297,
3464,
29898,
29900,
29892,
938,
29898,
5504,
29918,
10745,
23860,
22164,
13,
9651,
931,
29889,
17059,
29898,
29896,
29889,
29900,
29897,
13,
9651,
565,
330,
29883,
29889,
21174,
29918,
3707,
29901,
13,
18884,
2867,
13,
2
] |
hardware/opentrons_hardware/drivers/can_bus/can_messenger.py
|
mrod0101/opentrons
| 0 |
161649
|
<reponame>mrod0101/opentrons<filename>hardware/opentrons_hardware/drivers/can_bus/can_messenger.py
"""Can messenger class."""
import asyncio
from typing import List, Optional
import logging
from opentrons_hardware.drivers.can_bus import (
CanDriver,
NodeId,
ArbitrationId,
ArbitrationIdParts,
CanMessage,
MessageId,
)
from abc import ABC, abstractmethod
from opentrons_hardware.drivers.can_bus.messages.messages import (
MessageDefinition,
get_definition,
)
from opentrons_hardware.utils import BinarySerializableException
log = logging.getLogger(__name__)
class MessageListener(ABC):
"""Incoming message listener."""
@abstractmethod
def on_message(self, message: MessageDefinition) -> None:
"""A new message arrived."""
...
class CanMessenger:
"""High level can messaging class wrapping a CanDriver.
The background task can be controlled with start/stop methods.
To receive message notifications add a listener using add_listener
"""
def __init__(self, driver: CanDriver) -> None:
"""Constructor.
Args:
driver: The can bus driver to use.
"""
self._drive = driver
self._listeners: List[MessageListener] = []
self._task: Optional[asyncio.Task[None]] = None
async def send(self, node_id: NodeId, message: MessageDefinition) -> None:
"""Send a message."""
# TODO (amit, 2021-11-05): Use function code when it is better defined.
arbitration_id = ArbitrationId(
parts=ArbitrationIdParts(
message_id=message.message_id, node_id=node_id, function_code=0
)
)
data = message.payload.serialize()
log.debug(
f"Sending -->\n\tarbitration_id: {arbitration_id},\n\t"
f"payload: {message.payload}"
)
await self._drive.send(
message=CanMessage(arbitration_id=arbitration_id, data=data)
)
def start(self) -> None:
"""Start the reader task."""
if self._task:
log.warning("task already running.")
return
self._task = asyncio.get_event_loop().create_task(self._read_task())
async def stop(self) -> None:
"""Stop the reader task."""
if self._task:
self._task.cancel()
try:
await self._task
except asyncio.CancelledError:
log.info("Task cancelled.")
else:
log.warning("task not running.")
def add_listener(self, listener: MessageListener) -> None:
"""Add a message listener."""
self._listeners.append(listener)
def remove_listener(self, listener: MessageListener) -> None:
"""Remove a message listener."""
self._listeners.remove(listener)
async def _read_task(self) -> None:
"""Read task."""
async for message in self._drive:
message_definition = get_definition(
MessageId(message.arbitration_id.parts.message_id)
)
if message_definition:
try:
build = message_definition.payload_type.build(message.data)
log.debug(
f"Received <--\n\tarbitration_id: {message.arbitration_id},\n\t"
f"payload: {build}"
)
for listener in self._listeners:
listener.on_message(message_definition(payload=build)) # type: ignore[arg-type] # noqa: E501
except BinarySerializableException:
log.exception(f"Failed to build from {message}")
else:
log.error(f"Message {message} is not recognized.")
|
[
1,
529,
276,
1112,
420,
29958,
29885,
5964,
29900,
29896,
29900,
29896,
29914,
459,
296,
12628,
29966,
9507,
29958,
6800,
2519,
29914,
459,
296,
12628,
29918,
6800,
2519,
29914,
24477,
874,
29914,
3068,
29918,
8262,
29914,
3068,
29918,
29885,
9957,
914,
29889,
2272,
13,
15945,
29908,
6028,
4473,
15109,
770,
1213,
15945,
13,
5215,
408,
948,
3934,
13,
3166,
19229,
1053,
2391,
29892,
28379,
13,
5215,
12183,
13,
13,
3166,
1015,
296,
12628,
29918,
6800,
2519,
29889,
24477,
874,
29889,
3068,
29918,
8262,
1053,
313,
13,
1678,
1815,
12376,
29892,
13,
1678,
9071,
1204,
29892,
13,
1678,
826,
8844,
362,
1204,
29892,
13,
1678,
826,
8844,
362,
1204,
29925,
5708,
29892,
13,
1678,
1815,
3728,
29892,
13,
1678,
7777,
1204,
29892,
13,
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13,
3166,
25638,
1053,
16417,
29892,
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13,
13,
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1015,
296,
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29918,
6800,
2519,
29889,
24477,
874,
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19158,
29889,
19158,
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313,
13,
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13,
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13,
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13,
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1015,
296,
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13,
13,
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978,
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13,
13,
13,
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1125,
13,
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13,
13,
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732,
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5696,
13,
1678,
822,
373,
29918,
4906,
29898,
1311,
29892,
2643,
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2
] |
api.py
|
klapp101/GOAT
| 1 |
26525
|
import json
import requests
import time
from discord_webhook import DiscordWebhook, DiscordEmbed
webhook_url = 'https://discordapp.com/api/webhooks/672159508675690497/4UtaClAc7rKMJsEvbR4iYf-Razv4M3ZWtkYDOxBzLfiDzJhI7RSFpoLn6iijBiRcaNOR'
webhook = DiscordWebhook(webhook_url)
pid = '508214-660'
headers = {
'Connection': 'keep-alive',
'accept': 'application/json',
'Origin': 'https://www.goat.com',
'User-Agent': 'Mozilla/5.0 (Macintosh; Intel Mac OS X 10_14_6) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/79.0.3945.88 Safari/537.36',
'content-type': 'application/x-www-form-urlencoded',
'Sec-Fetch-Site': 'cross-site',
'Sec-Fetch-Mode': 'cors',
'Referer': 'https://www.goat.com/search?query='+ pid,
'Accept-Encoding': 'gzip, deflate, br',
'Accept-Language': 'en-US,en;q=0.9',
}
params = {
'x-algolia-agent': 'Algolia for vanilla JavaScript 3.25.1',
'x-algolia-application-id': '2FWOTDVM2O',
'x-algolia-api-key': 'ac96de6fef0e02bb95d433d8d5c7038a',
}
data = {
"distinct": 'true',
'facetFilters': 'product_category: shoes',
'facets': 'size',
'hitsPerPage': '48',
'numericFilters': '[]',
'page': '0',
'query': pid,
'clickAnalytics': "true"
}
response = requests.post('https://2fwotdvm2o-dsn.algolia.net/1/indexes/product_variants_v2/query', headers=headers, params=params,json=data)
response_json = response.json()
response_json_dict = response_json['hits'][0]
product_id = response_json_dict['product_template_id']
print(product_id)
def obtainBasicInfo():
webhook = DiscordWebhook(url=webhook_url)
r_api = requests.get('https://www.goat.com/web-api/v1/product_variants?productTemplateId='+ str(product_id),headers=headers)
data = r_api.json()
embed = DiscordEmbed(title=response_json_dict['name'], url=headers['Referer'], color=242424)
embed.set_thumbnail(url=response_json_dict['main_picture_url'])
sizes = []
shoe_conditions = []
box_conditions = []
prices = []
for i in data:
sizes.append(str(i['size']))
shoe_conditions.append(i['shoeCondition'])
box_conditions.append(i['boxCondition'])
prices.append(str(int(i['lowestPriceCents']['amountUsdCents'])/100))
print(' Size: ' + str(i['size']) + '\n' + ' Shoe condition: ' + i['shoeCondition'] + '\n' + ' Box condition: ' + i['boxCondition'] + '\n' + ' $' + str(int(i['lowestPriceCents']['amountUsdCents'])/100) + '\n' + '-----------------')
embed.add_embed_field(name='Size', value=(str(i['size'])))
embed.add_embed_field(name='Shoe Condition', value=str(i['shoeCondition']))
embed.add_embed_field(name='Box Condition', value=str(i['boxCondition']))
embed.add_embed_field(name='Price', value='$' + str(int(i['lowestPriceCents']['amountUsdCents'])/100))
webhook.add_embed(embed)
send_hook = webhook.execute()
time.sleep(2)
embed.fields = []
print(sizes)
print(shoe_conditions)
print(box_conditions)
print(prices)
obtainBasicInfo()
|
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580,
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2
] |
coptim/functions/rosenbrock.py
|
cmazzaanthony/Optimization_Algorithms
| 3 |
27697
|
<reponame>cmazzaanthony/Optimization_Algorithms
import numpy as np
from coptim.function import Function
class Rosenbrock(Function):
def eval(self, x):
assert len(x) == 2, '2 dimensional input only.'
return 100 * (x[1] - x[0] ** 2) ** 2 + (1 - x[0]) ** 2
def gradient(self, x):
assert len(x) == 2, '2 dimensional input only.'
return np.array([
2 * (-200 * x[0] * x[1] + 200 * np.power(x[0], 3) - 1 + x[0]),
200 * (x[1] - x[0] ** 2)
])
def hessian(self, x):
assert len(x) == 2, '2 dimensional input only.'
df_dx1 = -400 * x[1] + 1200 * x[0] ** 2 + 2
df_dx1dx2 = -400 * x[0]
df_dx2dx1 = -400 * x[0]
df_dx2 = 200
return np.array([[df_dx1, df_dx1dx2], [df_dx2dx1, df_dx2]])
|
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] |
tripleo_ansible/ansible_plugins/modules/tripleo_nova_image_cache.py
|
pavankrao/tripleo-ansible
| 0 |
1609587
|
<gh_stars>0
#!/usr/bin/python
# Copyright 2019 Red Hat, Inc.
# All Rights Reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License"); you may
# not use this file except in compliance with the License. You may obtain
# a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS, WITHOUT
# WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the
# License for the specific language governing permissions and limitations
# under the License.
from __future__ import absolute_import
__metaclass__ = type
from ansible.module_utils.basic import AnsibleModule
from ansible.module_utils.openstack import openstack_cloud_from_module
from ansible.module_utils.openstack import openstack_full_argument_spec
from ansible.module_utils.openstack import openstack_module_kwargs
import datetime
import hashlib
import os
import tempfile
import time
ANSIBLE_METADATA = {
'metadata_version': '1.1',
'status': ['preview'],
'supported_by': 'community'
}
DOCUMENTATION = '''
---
module: tripleo_nova_image_cache
short_description: Manage Nova image cache on TripleO OpenStack deployment
version_added: "2.0"
author: "<NAME> (@owalsh)"
description:
- Manage Nova image cache on TripleO OpenStack deployment
options:
id:
description:
- ID of the image to cache
required: true
ttl:
description:
- Ensure the image remains cached for at least ttl days
default: 7
state:
description:
- Whether the image be present in cache or expired
(nova should delete it later if it is no longer used)
choices: [present, expired]
default: present
scp_source:
description:
- Attempt to scp the image from this nova-compute host
scp_continue_on_error:
description:
- Fallback to image download if scp fails
default: false
requirements: ["openstacksdk"]
'''
EXAMPLES = '''
- name: Cache image for at least 2 weeks
tripleo_nova_image_cache:
id: ec151bd1-aab4-413c-b577-ced089e7d3f8
ttl: 14
- name: Allow nova to cleanup this image from cache
tripleo_nova_image_cache:
id: ec151bd1-aab4-413c-b577-ced089e7d3f8
state: expired
- name: Cache image, try to copy from existing host
tripleo_nova_image_cache:
id: ec151bd1-aab4-413c-b577-ced089e7d3f8
ttl: 14
scp_source: nova-compute-0
scp_continue_on_error: true
'''
def ttl_to_ts(ttl_days):
"""Return a timestamp at midnight UTC ttl_days in the future"""
epoch = datetime.datetime.utcfromtimestamp(0)
midnight_today = datetime.datetime.utcnow().replace(
hour=0, minute=0, second=0, microsecond=0)
expiry = midnight_today + datetime.timedelta(days=ttl_days+1)
return (expiry - epoch).total_seconds()
def main():
argument_spec = openstack_full_argument_spec(
id=dict(required=True),
ttl=dict(default=7, type='int', min=1),
state=dict(default='present', choices=['expired', 'present']),
_cache_dir=dict(required=True),
_cache_file=dict(required=True),
_chunk_size=dict(default=64 * 1024, type='int'),
_prefetched_path=dict(default=None),
scp_continue_on_error=dict(default=False, type='bool')
)
module_kwargs = openstack_module_kwargs()
module = AnsibleModule(argument_spec, **module_kwargs)
image_id = module.params['id']
cache_dir = module.params['_cache_dir']
cache_file = module.params['_cache_file']
chunk_size = module.params['_chunk_size']
prefetched_path = module.params['_prefetched_path']
scp_continue = module.params['scp_continue_on_error']
ttl_days = module.params['ttl']
result = dict(
changed=False,
actions=[],
image=None,
cache_file='',
exists_in_cache=False,
mtime=0
)
sdk, cloud = openstack_cloud_from_module(module, min_version='0.11.3')
try:
result['exists_in_cache'] = exists_in_cache = os.path.exists(
cache_file)
if exists_in_cache:
result['cache_file'] = cache_file
image = cloud.image.find_image(name_or_id=image_id)
exists_in_glance = image is not None
if exists_in_glance:
result['image'] = image.to_dict()
if module.params['state'] == 'present':
if not exists_in_cache:
if not exists_in_glance:
module.fail_json(
msg="Image not found in glance: %s" % image_id)
md5 = hashlib.md5()
if prefetched_path:
result['actions'].append({
'name': 'Verify pre-fetched image checksum'
})
with open(prefetched_path, 'rb') as prefetched_image_file:
while True:
chunk = prefetched_image_file.read(chunk_size)
if not chunk:
break
md5.update(chunk)
prefetched_checksum = md5.hexdigest()
if prefetched_checksum == image.checksum:
result['actions'].append({
'name': 'Verify pre-fetched image',
'result': True,
'expected_md5': image.checksum,
'actual_md5': prefetched_checksum
})
# FIXME: chown to the container nova uid (42436)
# until we can run within the container
os.chown(prefetched_path, 42436, 42436)
os.rename(prefetched_path, cache_file)
else:
result['actions'].append({
'name': 'Verify pre-fetched image',
'result': False,
'expected_md5': image.checksum,
'actual_md5': prefetched_checksum
})
if not scp_continue:
module.fail_json(
msg="Pre-fetched image checksum failed")
# Ignore it and download direct from glance.
# As we did not create it we should not remove it.
prefetched_path = ''
if not prefetched_path:
with tempfile.NamedTemporaryFile(
'wb',
dir=cache_dir,
delete=False) as temp_cache_file:
try:
md5 = hashlib.md5()
image_stream = cloud.image.download_image(
image,
stream=True
)
try:
for chunk in image_stream.iter_content(
chunk_size=chunk_size):
md5.update(chunk)
temp_cache_file.write(chunk)
finally:
image_stream.close()
temp_cache_file.close()
download_checksum = md5.hexdigest()
if download_checksum != image.checksum:
result['actions'].append({
'name': 'Verify downloaded image',
'result': False,
'expected_md5': image.checksum,
'actual_md5': download_checksum
})
module.fail_json(
msg="Image data does not match checksum")
result['actions'].append({
'name': 'Verify downloaded image',
'result': True,
'expected_md5': image.checksum,
'actual_md5': download_checksum
})
# FIXME: chown to the container nova uid (42436)
# until we can run within the container
os.chown(temp_cache_file.name, 42436, 42436)
os.rename(temp_cache_file.name, cache_file)
result['changed'] = True
finally:
try:
os.unlink(temp_cache_file.name)
except Exception:
pass
# Set the mtime in the future to prevent nova cleanup
cache_file_stat = os.stat(cache_file)
expiry_ts = ttl_to_ts(ttl_days)
now = time.time()
if cache_file_stat.st_mtime != expiry_ts:
os.utime(cache_file, (now, expiry_ts))
result['actions'].append({
'name': 'Update mtime',
'from': cache_file_stat.st_mtime,
'to': expiry_ts
})
result['changed'] = True
else: # expired
if not exists_in_cache:
result['changed'] = False
else:
# Set the mtime to epoch to enable nova cleanup
now = time.time()
ts = 0
cache_file_stat = os.stat(cache_file)
if cache_file_stat.st_mtime > ts:
os.utime(cache_file, (now, ts))
result['actions'].append({
'name': 'Update mtime',
'from': cache_file_stat.st_mtime,
'to': ts
})
result['changed'] = True
cache_file_stat = os.stat(cache_file)
result['mtime'] = cache_file_stat.st_mtime
result['expires'] = time.strftime(
"%a, %d %b %Y %H:%M:%S %z",
time.localtime(cache_file_stat.st_mtime)
)
module.exit_json(**result)
except sdk.exceptions.OpenStackCloudException as e:
module.fail_json(msg=str(e), extra_data=e.extra_data)
if __name__ == "__main__":
main()
|
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] |
typical90/aj/main.py
|
KATO-Hiro/AtCoder
| 2 |
189232
|
# -*- coding: utf-8 -*-
def main():
import sys
input = sys.stdin.readline
n, q = map(int, input().split())
x = [0] * n
y = [0] * n
for i in range(n):
xi, yi = map(int, input().split())
x[i] = xi + yi
y[i] = xi - yi
x_min, x_max = min(x), max(x)
y_min, y_max = min(y), max(y)
for j in range(q):
qi = int(input())
qi -= 1
xi = x[qi]
yi = y[qi]
print(max(x_max - xi, xi - x_min, y_max - yi, yi - y_min))
if __name__ == "__main__":
main()
|
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1115,
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1678,
1667,
580,
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2
] |
RecoVertex/BeamSpotProducer/test/BeamFit_LumiBased_NewAlignWorkflow.py
|
ckamtsikis/cmssw
| 852 |
148337
|
import FWCore.ParameterSet.Config as cms
process = cms.Process("BSworkflow")
process.source = cms.Source("PoolSource",
fileNames = cms.untracked.vstring(
"/store/express/Run2015A/StreamExpress/ALCARECO/TkAlMinBias-Express-v1/000/246/959/00000/14174DF2-490A-E511-9862-02163E0143E9.root",
)
)
process.load("FWCore.MessageLogger.MessageLogger_cfi")
process.MessageLogger.cerr.FwkReport = cms.untracked.PSet(
reportEvery = cms.untracked.int32(10000),
)
process.MessageLogger.debugModules = ['BeamSpotAnalyzer']
process.maxEvents = cms.untracked.PSet(
input = cms.untracked.int32(-1)
)
process.options = cms.untracked.PSet(
wantSummary = cms.untracked.bool(True)
)
process.load("RecoVertex.BeamSpotProducer.BeamSpot_cfi")
process.load("Configuration.StandardSequences.MagneticField_cff")
process.load('Configuration.Geometry.GeometryRecoDB_cff')
process.load("Configuration.StandardSequences.FrontierConditions_GlobalTag_cff")
# this GT is for the Express, to be consistent with the file above
# in general this GT should be for the ReReco
process.GlobalTag.globaltag = 'GR_E_V48'
## Track refit
process.load("RecoTracker.TrackProducer.TrackRefitters_cff")
# remove the following lines if you run on RECO files
process.TrackRefitter.src = 'ALCARECOTkAlMinBias'
process.TrackRefitter.NavigationSchool = ''
## PV refit
process.load("TrackingTools.TransientTrack.TransientTrackBuilder_cfi")
from RecoVertex.PrimaryVertexProducer.OfflinePrimaryVertices_cfi import offlinePrimaryVertices
process.offlinePrimaryVerticesFromRefittedTrks = offlinePrimaryVertices.clone()
process.offlinePrimaryVerticesFromRefittedTrks.TrackLabel = cms.InputTag("TrackRefitter")
process.offlinePrimaryVerticesFromRefittedTrks.vertexCollections.maxDistanceToBeam = 1
process.offlinePrimaryVerticesFromRefittedTrks.TkFilterParameters.maxNormalizedChi2 = 20
process.offlinePrimaryVerticesFromRefittedTrks.TkFilterParameters.minSiliconLayersWithHits = 5
process.offlinePrimaryVerticesFromRefittedTrks.TkFilterParameters.maxD0Significance = 5.0
process.offlinePrimaryVerticesFromRefittedTrks.TkFilterParameters.minPixelLayersWithHits = 2
## BeamSpot fit
process.load("RecoVertex.BeamSpotProducer.d0_phi_analyzer_cff")
process.d0_phi_analyzer.BeamFitter.WriteAscii = True
process.d0_phi_analyzer.BeamFitter.AsciiFileName = 'BeamFit_LumiBased_NewAlignWorkflow_alcareco.txt'
process.d0_phi_analyzer.BeamFitter.AppendRunToFileName = False
process.d0_phi_analyzer.BeamFitter.InputBeamWidth = -1
process.d0_phi_analyzer.BeamFitter.MaximumImpactParameter = 1.0
process.d0_phi_analyzer.BeamFitter.MaximumNormChi2 = 10
process.d0_phi_analyzer.BeamFitter.MinimumInputTracks = 50
process.d0_phi_analyzer.BeamFitter.MinimumPixelLayers = -1
process.d0_phi_analyzer.BeamFitter.MinimumPt = 1.0
process.d0_phi_analyzer.BeamFitter.MinimumTotalLayers = 6
process.d0_phi_analyzer.BeamFitter.OutputFileName = 'BeamFit_LumiBased_Workflow_alcareco.root'
process.d0_phi_analyzer.BeamFitter.TrackAlgorithm = cms.untracked.vstring()
process.d0_phi_analyzer.BeamFitter.TrackCollection = 'TrackRefitter'
process.d0_phi_analyzer.BeamFitter.SaveFitResults = True
process.d0_phi_analyzer.BeamFitter.SaveNtuple = False
process.d0_phi_analyzer.BeamFitter.SavePVVertices = True
process.d0_phi_analyzer.PVFitter.Apply3DFit = True
process.d0_phi_analyzer.PVFitter.minNrVerticesForFit = 10
process.d0_phi_analyzer.PVFitter.nSigmaCut = 50.0
process.d0_phi_analyzer.PVFitter.VertexCollection = 'offlinePrimaryVerticesFromRefittedTrks'
process.d0_phi_analyzer.BSAnalyzerParameters.fitEveryNLumi = 1
process.d0_phi_analyzer.BSAnalyzerParameters.resetEveryNLumi = 1
process.p = cms.Path(process.offlineBeamSpot +
process.TrackRefitter +
process.offlinePrimaryVerticesFromRefittedTrks +
process.d0_phi_analyzer)
|
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] |
tower/utils/enemygen.py
|
alisonbnt/watchtower
| 1 |
178662
|
from ..creature import Enemy
class EnemyGenerator(object):
def __init__(self):
self.health = None
self.block = None
self.hit = None
def generate_enemy(self, creature, choices):
species = creature.get_species()
self.health = species['health']
self.block = species['block']
self.hit = species['hit']
self.guesses = {}
aspect_type = 'materials'
self.get_modifier(creature, aspect_type, choices[aspect_type])
aspect_type = 'places'
self.get_modifier(creature, aspect_type, choices[aspect_type])
aspect_type = 'summonings'
self.get_modifier(creature, aspect_type, choices[aspect_type])
aspect_type = 'banishments'
self.get_modifier(creature, aspect_type, choices[aspect_type])
aspect_type = 'amulets'
self.get_element_modifier(creature, aspect_type, choices[aspect_type])
aspect_type = 'potions'
self.get_element_modifier(creature, aspect_type, choices[aspect_type])
return Enemy(self.health, self.block, self.hit, self.guesses)
def get_modifier(self, creature, aspect_type, item):
if item in creature.get_aspect_indexes(aspect_type):
self.guesses.update({aspect_type: True})
aspect = creature.get_aspect(aspect_type, item)
if aspect['effect'] == 'health':
self.health += int(aspect['power'] * -1)
elif aspect['effect'] == 'hit':
self.hit += int(aspect['power'])
else:
self.guesses.update({aspect_type: False})
def get_element_modifier(self, creature, aspect_type, item):
elements = creature.get_element_indexes(aspect_type)
if item in elements:
self.guesses.update({aspect_type: True})
aspect = creature.get_element_aspect(aspect_type, item)
if aspect['effect'] == 'health':
self.health += int(creature.get_element_value(aspect_type,
'health') * -1)
elif aspect['effect'] == 'hit':
self.hit += int(creature.get_element_value(aspect_type, 'hit'))
else:
self.guesses.update({aspect_type: False})
|
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5504,
3319,
294,
1103,
29918,
1853,
29901,
7700,
1800,
13,
2
] |
DallalKosh/app/migrations/0009_auto_20171108_0603.py
|
CarenZarin/DallalKosh
| 1 |
1605333
|
<reponame>CarenZarin/DallalKosh
# -*- coding: utf-8 -*-
# Generated by Django 1.11.7 on 2017-11-08 06:03
from __future__ import unicode_literals
from django.db import migrations
class Migration(migrations.Migration):
dependencies = [
('app', '0008_auto_20171108_0549'),
]
operations = [
migrations.RenameField(
model_name='good',
old_name='good_data',
new_name='good_date',
),
]
|
[
1,
529,
276,
1112,
420,
29958,
8179,
264,
29999,
27139,
29914,
29928,
497,
284,
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13,
29937,
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29930,
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2433,
16773,
29918,
1256,
742,
13,
4706,
10353,
13,
1678,
4514,
13,
2
] |
holmes/migrations/versions/21087e990aa8_change_violation_to_.py
|
scorphus/holmes-api
| 0 |
187383
|
<filename>holmes/migrations/versions/21087e990aa8_change_violation_to_.py
"""Change violation to contain less info
Revision ID: 21087e990aa8
Revises: <PASSWORD>
Create Date: 2013-12-02 22:11:34.036524
"""
# revision identifiers, used by Alembic.
revision = '21087e990aa8'
down_revision = '2<PASSWORD>'
from alembic import op
import sqlalchemy as sa
def upgrade():
op.drop_column('violations', 'title')
op.drop_column('violations', 'description')
op.add_column('violations', sa.Column('value', sa.Text, nullable=True))
def downgrade():
op.add_column('violations', sa.Column('title', sa.String(2000), nullable=False))
op.add_column('violations', sa.Column('description', sa.Text, nullable=False))
op.drop_column('violations', 'value')
|
[
1,
529,
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29958,
5391,
4467,
29914,
26983,
800,
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29906,
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29918,
4914,
877,
1403,
324,
800,
742,
525,
1767,
1495,
13,
2
] |
bot_facebook/views/__init__.py
|
lariodiniz/BootPlayGame
| 0 |
70120
|
<reponame>lariodiniz/BootPlayGame
from .ativa_bot_view import ativaBotView
|
[
1,
529,
276,
1112,
420,
29958,
29880,
1306,
397,
262,
466,
29914,
20967,
13454,
14199,
13,
3166,
869,
8657,
29918,
7451,
29918,
1493,
1053,
472,
4244,
29933,
327,
1043,
2
] |
transform_username.py
|
bbhunter/username-generation-guide
| 38 |
132948
|
<filename>transform_username.py
#!/usr/bin/env python3
import argparse
import os
import re
class Rule:
def __str__(self):
return f'{self.action}: {self.arg1}, {self.arg2}'
def load_rules(filename):
lines = open(filename).read().splitlines()
for line in lines:
l = line
if not l or l.startswith('#'):
continue
data = re.split('( from | to )', l)
if len(data) != 5:
print(data)
print(f'Invalid rule line: {l}')
continue
r = Rule()
r.action = data[0].strip()
r.arg1 = data[2].strip()
r.arg2 = data[4].strip()
yield r
def apply_rule(username, rule):
results = set({username})
once_sign = False
if rule.action == 'replace-any-case':
start_pos = 0
while True:
try:
index = username.lower().index(rule.arg1.lower(), start_pos)
except:
break
left, right = username[:start_pos], username[start_pos:]
right = right.lower().replace(rule.arg1.lower(), rule.arg2, 1)
results.add(left+right)
start_pos = index + 1
if index+len(rule.arg1) > len(username):
break
elif rule.action == 'replace':
start_pos = 0
while True:
try:
index = username.index(rule.arg1, start_pos)
except:
break
left, right = username[:start_pos], username[start_pos:]
right = right.replace(rule.arg1, rule.arg2, 1)
results.add(left+right)
start_pos = index + 1
if index+len(rule.arg1) > len(username):
break
elif rule.action == 'change-case':
for i in range(len(username)):
new_name = username
new_name = new_name[:i] + new_name[i].swapcase() + new_name[i+1:]
results.add(new_name)
elif rule.action == 'append':
if rule.arg2 == 'right' and not username.endswith(rule.arg1):
results.add(username + rule.arg1)
elif rule.arg2 == 'left' and not username.startswith(rule.arg1):
results.add(rule.arg1 + username)
elif rule.arg2 == 'both':
name = username
if not username.endswith(rule.arg1):
name = rule.arg1 + name
if not username.startswith(rule.arg1):
name = name + rule.arg1
results.add(name)
once_sign = True
elif rule.action == 'remove-pos':
pos = int(rule.arg1)
new_username = username[:pos] + username[pos+1:]
results.add(new_username)
once_sign = True
return results, once_sign
def process_rules_recursive(usernames, rules, results):
for username in usernames:
for r in rules:
res, once_sign = apply_rule(username, r)
new_res = res.difference(results)
if once_sign:
results = results | new_res
continue
if not new_res:
continue
for result in new_res:
rec_res = process_rules_recursive(set({result}), rules, results | new_res)
results = results | rec_res | new_res
return results
def process_rules(usernames, rules):
return process_rules_recursive(usernames, rules, usernames)
if __name__ == '__main__':
parser = argparse.ArgumentParser(
formatter_class=argparse.RawDescriptionHelpFormatter,
description="Username mutation script",
)
username_group = parser.add_mutually_exclusive_group(required=True)
username_group.add_argument(
'--username',
help="Username to mutate",
)
username_group.add_argument(
'--username-list',
help="Filename of username list to mutate",
)
username_group.add_argument(
'-I',
'--username-input',
action='store_true',
default=False,
help="Get usernames from stdin",
)
parser.add_argument(
'rule_filename',
type=str,
help="Rule to mutate (see rules directory)",
)
parser.add_argument(
'--remove-known',
default=False,
action='store_true',
help="Remove known usernames from output",
)
args = parser.parse_args()
if args.username:
usernames = set({args.username})
elif args.username_list:
usernames = set(open(args.username_list).read().splitlines())
else:
import sys
usernames = set()
for line in sys.stdin:
usernames.add(line.strip())
rules = list(load_rules(args.rule_filename))
new_usernames = process_rules(usernames, rules)
if args.remove_known:
usernames = new_usernames.difference(usernames)
else:
usernames = new_usernames
print('\n'.join(usernames))
|
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824,
1280,
876,
13,
2
] |
Xana/ProcData/CbfMethods.py
|
ClLov/Xana
| 0 |
148130
|
<gh_stars>0
from os.path import isfile
import cbf
####################################
#--- Standard cbf w/r functions ---#
####################################
def loadcbf(filename):
if isfile(filename):
f = cbf.read(filename)
return f.data
else:
print("file ", filename, " does not exist!")
return 0
def savecbf(filename, data):
if not isfile(filename):
cbf.write(filename, data)
else:
print("file ", filename, " does already exist!")
return None
def headercbf(filename):
if isfile(filename):
f = cbf.read(filename)
return f.metadata
else:
print("file ", filename, " does not exist!")
return 0
|
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2
] |
performance/driver/classes/channel/cmdline.py
|
mesosphere/dcos-perf-test-driver
| 2 |
90274
|
import fcntl
import os
import select
import shlex
import signal
import threading
import time
from subprocess import Popen, PIPE
from performance.driver.core.events import Event, LogLineEvent, ParameterUpdateEvent, TeardownEvent, StartEvent
from performance.driver.core.template import TemplateString, TemplateDict
from performance.driver.core.classes import Channel
from performance.driver.core.reflection import subscribesToHint, publishesHint
from performance.driver.core.utils import parseTimeExpr
class CmdlineExitEvent(Event):
"""
This event is published when the process launched through the cmdline channel
has completed. The exit code is tracked.
"""
def __init__(self, exitcode, **kwargs):
super().__init__(**kwargs)
#: The exit code of the application launched by the command-line channel
self.exitcode = exitcode
class CmdlineExitZeroEvent(CmdlineExitEvent):
"""
This event is published when the process exited and the exit code
is zero
"""
class CmdlineExitNonzeroEvent(CmdlineExitEvent):
"""
This event is published when the process exited and the exit code
is non-zero
"""
class CmdlineStartedEvent(Event):
"""
This event is published when the process has started. It contains the process
ID so the observers can attach to the process and extract useful data.
"""
def __init__(self, pid, *args, **kwargs):
super().__init__(*args, **kwargs)
self.pid = pid
class CmdlineChannel(Channel):
"""
The *Command-line Channel* launches an application, passes the test parameters
through command-line arguments and monitors it's standard output and error.
::
channels:
- class: channel.CmdlineChannel
# The command-line to launch.
cmdline: "path/to/app --args {{macros}}"
# [Optional] The standard input to send to the application
stdin: |
some arbitrary payload with {{macros}}
in it's body.
# [Optional] Environment variables to define
env:
variable: value
other: "value with {{macros}}"
# [Optional] The directory to launch this app in
cwd: "{{marathon_repo_dir}}"
# [Optional] The script to use for teardown
teardown: stop_daemon
# [Optional] If set to `yes` the app will be launched as soon
# as the driver is up and running.
atstart: yes
# [Optional] If set to `yes` (default) the app will be re-launched
# if it exits on it's own.
restart: yes
# [Optional] If set to `yes` the "cmdline" expression will be evalued
# in a shell.
shell: no
# [Optional] The time between the SIGINT and the SIGKILL signal
gracePeriod: 10s
# [Optional] Change the `kind` of log messages emitted by this channel
# instead of using the default 'stdout' / 'stderr'
kind:
stdout: custom_out
stderr: custom_err
When a parameter is changed, the channel will kill the process and re-launch
it with the new command-line.
For every line in standard inout or output, a ``LogLineEvent`` is emitted
with the contents of the line.
When the application launched through this channel exits the channel can take
two actions depending on it's configuration:
* If ``restart: yes`` is specitied (default), the channel will re-launch the
application in oder to always keep it running.
* If ``restart: no`` is specified, the channel will give up and publish a
``CmdlineExitZeroEvent`` or a ``CmdlineExitNonzeroEvent`` according to the
exit code of the application.
.. note::
Note that if there are no ``{{macro}}`` defined anywhere in the body of
the configuration this channel will not be triggered when a parameter
is updated and thus the application will never be launched.
If you still want the application to be launched, use the ``atstart: yes``
parameter to instruct the channel to launch the application at start.
"""
@subscribesToHint(ParameterUpdateEvent, TeardownEvent, StartEvent)
def __init__(self, *args, **kwargs):
super().__init__(*args, **kwargs)
self.activeTask = None
self.activeParameters = {}
self.killing = False
self.lastTraceId = None
# Receive parameter updates and clean-up on teardown
self.eventbus.subscribe(self.handleTeardown, events=(TeardownEvent, ))
self.eventbus.subscribe(self.handleStart, events=(StartEvent, ))
# Get some template
self.cmdlineTpl = TemplateString(self.getConfig('cmdline'))
self.stdinTpl = TemplateString(self.getConfig('stdin', ''))
self.envTpl = TemplateDict(self.getConfig('env', {}))
self.cwdTpl = TemplateString(self.getConfig('cwd', ''))
self.graceTpl = TemplateString(self.getConfig('gracePeriod', '10s'))
self.teardownTpl = TemplateString(self.getConfig('teardown', ''))
# Get some options
config = self.getRenderedConfig()
self.kindMap = config.get('kind', {})
if not 'stdout' in self.kindMap:
self.kindMap['stdout'] = 'stdout'
if not 'stderr' in self.kindMap:
self.kindMap['stderr'] = 'stderr'
@publishesHint(LogLineEvent, CmdlineExitEvent, CmdlineExitZeroEvent,
CmdlineExitNonzeroEvent)
def monitor(self, sourceName, proc, stdin=None):
"""
Oversees the execution of the process
"""
lines = ['', '']
# Make read operations non-blocking
flag = fcntl.fcntl(proc.stdout.fileno(), fcntl.F_GETFD)
fcntl.fcntl(proc.stdout.fileno(), fcntl.F_SETFL, flag | os.O_NONBLOCK)
flag = fcntl.fcntl(proc.stderr.fileno(), fcntl.F_GETFD)
fcntl.fcntl(proc.stderr.fileno(), fcntl.F_SETFL, flag | os.O_NONBLOCK)
# Send stdin
if stdin and not proc.stdin is None:
proc.stdin.write(stdin)
proc.stdin.close()
# Wait for input in the FDs
while True:
if proc.poll() is None:
# While process is running, use `select` to wait for an stdout/err
# FD event before reading.
(rlist, wlist, xlist) = select.select([proc.stdout, proc.stderr], [],
[])
if proc.stdout in rlist:
block = proc.stdout.read(1024 * 1024)
lines[0] += block.decode('utf-8')
while '\n' in lines[0]:
(line, lines[0]) = lines[0].split('\n', 1)
self.eventbus.publish(
LogLineEvent(
line, sourceName, self.kindMap['stdout'],
traceid=self.lastTraceId))
if proc.stderr in rlist:
block = proc.stderr.read(1024 * 1024)
lines[1] += block.decode('utf-8')
while '\n' in lines[1]:
(line, lines[1]) = lines[1].split('\n', 1)
self.eventbus.publish(
LogLineEvent(
line, sourceName, self.kindMap['stderr'],
traceid=self.lastTraceId))
else:
# Drain buffers since after the process has exited, select might not work
# and some remaining bytes will remain not processed.
block = proc.stdout.read()
if block:
lines[0] += block.decode('utf-8')
for line in lines[0].split('\n'):
if line.strip():
self.eventbus.publish(
LogLineEvent(
line, sourceName, self.kindMap['stdout'],
traceid=self.lastTraceId))
block = proc.stderr.read()
if block:
lines[1] += block.decode('utf-8')
for line in lines[0].split('\n'):
if line.strip():
self.eventbus.publish(
LogLineEvent(
line, sourceName, self.kindMap['stderr'],
traceid=self.lastTraceId))
# Break loop
break
# Mark as stopped
self.activeTask = None
if not self.killing:
# Dispatch the correct exit message
self.logger.debug('Process exited with code {}'.format(proc.returncode))
if proc.returncode == 0:
self.eventbus.publish(
CmdlineExitZeroEvent(proc.returncode, traceid=self.lastTraceId))
else:
self.eventbus.publish(
CmdlineExitNonzeroEvent(proc.returncode, traceid=self.lastTraceId))
# Restart if configured
if self.getConfig('restart', True):
self.logger.warn('Process exited prematurely')
self.launch(self.activeParameters)
else:
self.logger.info('Process completed')
def kill(self):
"""
Kill the currently running proc
"""
if not self.activeTask:
return
# Get grace period
macroValues = self.getDefinitions()
gracePeriod = parseTimeExpr(self.graceTpl.apply(macroValues))
shell = self.getConfig('shell', False)
# If we have a tear-down command, launch it now
teardownCmd = self.teardownTpl.apply(macroValues)
if teardownCmd:
env = self.envTpl.apply(macroValues)
if not env:
env = None
cwd = self.cwdTpl.apply(macroValues)
if not cwd:
cwd = None
# Launch teardown process
self.logger.info('Launching tear-down command: {}'.format(teardownCmd))
proc = Popen(
teardownCmd,
env=env,
cwd=cwd,
shell=True,
preexec_fn=os.setsid)
# Wait for the command to exit
exitcode = proc.wait()
self.logger.info('Tear-down process completed with {}'.format(exitcode))
# Then, try interrupting the process
self.killing = True
proc, thread = self.activeTask
try:
if proc.poll() is None:
self.logger.debug('Sending SIGINT')
os.killpg(os.getpgid(proc.pid), signal.SIGINT)
except ProcessLookupError:
pass
# Wait for a grace period
timeout = time.time() + gracePeriod
self.logger.debug('Waiting for app to be terminated')
while proc.poll() is None and time.time() < timeout:
time.sleep(1)
# And try killing it
try:
if proc.poll() is None:
self.logger.debug('Sending SIGKILL')
os.killpg(os.getpgid(proc.pid), signal.SIGTERM)
except ProcessLookupError:
pass
thread.join()
# Unset active task
self.activeTask = None
self.killing = False
def launch(self, parameters):
"""
Launch process with the given command-line
"""
# Compose the arguments for the execution
cwd = self.getConfig('cwd', required=False)
shell = self.getConfig('shell', False)
# Combine parameters with the definitions
macroValues = self.getDefinitions().fork(parameters)
# Compile arguments
cmdline = self.cmdlineTpl.apply(macroValues)
if not shell:
args = shlex.split(cmdline)
else:
args = cmdline
stdin = self.stdinTpl.apply(macroValues)
env = self.envTpl.apply(macroValues)
cwd = self.cwdTpl.apply(macroValues)
# Reset empty arguments to `None`
if not stdin:
stdin = None
if not cwd:
cwd = None
if not env:
env = None
# Launch
self.logger.debug('Starting process: \'{}\''.format(
args if type(args) is str else ' '.join(args)))
self.activeParameters = parameters
proc = Popen(
args,
stdin=PIPE,
stdout=PIPE,
stderr=PIPE,
env=env,
cwd=cwd,
shell=shell,
preexec_fn=os.setsid)
# Dispatch start event
self.eventbus.publish(CmdlineStartedEvent(proc.pid))
# Launch a thread to monitor it's output
thread = threading.Thread(
target=self.monitor,
name='cmdline-monitor',
args=(args[0], proc, stdin))
thread.start()
# Keep track of the active task
self.activeTask = (proc, thread)
def handleTeardown(self, event):
"""
Kill process at teardown
"""
if self.activeTask:
self.logger.warn('Killing running process')
self.kill()
def handleStart(self, event):
"""
Handle the start event
"""
# If this app is instructed to launch at start, satisfy this requirement now
atstart = self.getConfig('atstart', default=False)
if atstart:
self.handleParameterUpdate(ParameterUpdateEvent({}, {}, {}))
def handleParameterUpdate(self, event):
"""
Handle a property update
"""
# If we have already a task running don't re-launch it unless
# the received properties are actually updating one or more parameters
# in our template
if self.activeTask:
# We have a parameter change, kill the process
# and schedule a new execution later
self.kill()
# Keep track of the traceid that initiated the process. This way we can
# track the events that were derrived from this parameter update.
self.lastTraceId = event.traceids
# Launch new process
self.launch(event.parameters)
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] |
functest/opnfv_tests/openstack/tempest/conf_utils.py
|
hashnfv/hashnfv-functest
| 0 |
98069
|
<reponame>hashnfv/hashnfv-functest
#!/usr/bin/env python
#
# Copyright (c) 2015 All rights reserved
# This program and the accompanying materials
# are made available under the terms of the Apache License, Version 2.0
# which accompanies this distribution, and is available at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
import ConfigParser
import logging
import os
import pkg_resources
import shutil
import subprocess
import yaml
from functest.utils.constants import CONST
import functest.utils.functest_utils as ft_utils
import functest.utils.openstack_utils as os_utils
IMAGE_ID_ALT = None
FLAVOR_ID_ALT = None
GLANCE_IMAGE_PATH = os.path.join(
CONST.__getattribute__('dir_functest_images'),
CONST.__getattribute__('openstack_image_file_name'))
TEMPEST_RESULTS_DIR = os.path.join(CONST.__getattribute__('dir_results'),
'tempest')
TEMPEST_CUSTOM = pkg_resources.resource_filename(
'functest', 'opnfv_tests/openstack/tempest/custom_tests/test_list.txt')
TEMPEST_BLACKLIST = pkg_resources.resource_filename(
'functest', 'opnfv_tests/openstack/tempest/custom_tests/blacklist.txt')
TEMPEST_DEFCORE = pkg_resources.resource_filename(
'functest',
'opnfv_tests/openstack/tempest/custom_tests/defcore_req.txt')
TEMPEST_RAW_LIST = os.path.join(TEMPEST_RESULTS_DIR, 'test_raw_list.txt')
TEMPEST_LIST = os.path.join(TEMPEST_RESULTS_DIR, 'test_list.txt')
REFSTACK_RESULTS_DIR = os.path.join(CONST.__getattribute__('dir_results'),
'refstack')
TEMPEST_CONF_YAML = pkg_resources.resource_filename(
'functest', 'opnfv_tests/openstack/tempest/custom_tests/tempest_conf.yaml')
TEST_ACCOUNTS_FILE = pkg_resources.resource_filename(
'functest',
'opnfv_tests/openstack/tempest/custom_tests/test_accounts.yaml')
CI_INSTALLER_TYPE = CONST.__getattribute__('INSTALLER_TYPE')
CI_INSTALLER_IP = CONST.__getattribute__('INSTALLER_IP')
""" logging configuration """
logger = logging.getLogger(__name__)
def get_verifier_id():
"""
Returns verifier id for current Tempest
"""
cmd = ("rally verify list-verifiers | awk '/" +
CONST.__getattribute__('tempest_deployment_name') +
"/ {print $2}'")
p = subprocess.Popen(cmd, shell=True,
stdout=subprocess.PIPE,
stderr=subprocess.STDOUT)
deployment_uuid = p.stdout.readline().rstrip()
if deployment_uuid == "":
logger.error("Tempest verifier not found.")
raise Exception('Error with command:%s' % cmd)
return deployment_uuid
def get_verifier_deployment_id():
"""
Returns deployment id for active Rally deployment
"""
cmd = ("rally deployment list | awk '/" +
CONST.__getattribute__('rally_deployment_name') +
"/ {print $2}'")
p = subprocess.Popen(cmd, shell=True,
stdout=subprocess.PIPE,
stderr=subprocess.STDOUT)
deployment_uuid = p.stdout.readline().rstrip()
if deployment_uuid == "":
logger.error("Rally deployment not found.")
raise Exception('Error with command:%s' % cmd)
return deployment_uuid
def get_verifier_repo_dir(verifier_id):
"""
Returns installed verifier repo directory for Tempest
"""
if not verifier_id:
verifier_id = get_verifier_id()
return os.path.join(CONST.__getattribute__('dir_rally_inst'),
'verification',
'verifier-{}'.format(verifier_id),
'repo')
def get_verifier_deployment_dir(verifier_id, deployment_id):
"""
Returns Rally deployment directory for current verifier
"""
if not verifier_id:
verifier_id = get_verifier_id()
if not deployment_id:
deployment_id = get_verifier_deployment_id()
return os.path.join(CONST.__getattribute__('dir_rally_inst'),
'verification',
'verifier-{}'.format(verifier_id),
'for-deployment-{}'.format(deployment_id))
def backup_tempest_config(conf_file):
"""
Copy config file to tempest results directory
"""
if not os.path.exists(TEMPEST_RESULTS_DIR):
os.makedirs(TEMPEST_RESULTS_DIR)
shutil.copyfile(conf_file,
os.path.join(TEMPEST_RESULTS_DIR, 'tempest.conf'))
def configure_tempest(deployment_dir, image_id=None, flavor_id=None,
compute_cnt=None):
"""
Calls rally verify and updates the generated tempest.conf with
given parameters
"""
conf_file = configure_verifier(deployment_dir)
configure_tempest_update_params(conf_file, image_id, flavor_id,
compute_cnt)
def configure_tempest_defcore(deployment_dir, image_id, flavor_id,
image_id_alt, flavor_id_alt, tenant_id):
"""
Add/update needed parameters into tempest.conf file
"""
conf_file = configure_verifier(deployment_dir)
configure_tempest_update_params(conf_file, image_id, flavor_id)
logger.debug("Updating selected tempest.conf parameters for defcore...")
config = ConfigParser.RawConfigParser()
config.read(conf_file)
config.set('DEFAULT', 'log_file', '{}/tempest.log'.format(deployment_dir))
config.set('oslo_concurrency', 'lock_path',
'{}/lock_files'.format(deployment_dir))
generate_test_accounts_file(tenant_id=tenant_id)
config.set('auth', 'test_accounts_file', TEST_ACCOUNTS_FILE)
config.set('scenario', 'img_dir', '{}'.format(deployment_dir))
config.set('scenario', 'img_file', 'tempest-image')
config.set('compute', 'image_ref', image_id)
config.set('compute', 'image_ref_alt', image_id_alt)
config.set('compute', 'flavor_ref', flavor_id)
config.set('compute', 'flavor_ref_alt', flavor_id_alt)
with open(conf_file, 'wb') as config_file:
config.write(config_file)
confpath = pkg_resources.resource_filename(
'functest',
'opnfv_tests/openstack/refstack_client/refstack_tempest.conf')
shutil.copyfile(conf_file, confpath)
def generate_test_accounts_file(tenant_id):
"""
Add needed tenant and user params into test_accounts.yaml
"""
logger.debug("Add needed params into test_accounts.yaml...")
accounts_list = [
{
'tenant_name':
CONST.__getattribute__('tempest_identity_tenant_name'),
'tenant_id': str(tenant_id),
'username': CONST.__getattribute__('tempest_identity_user_name'),
'password':
CONST.__getattribute__('tempest_identity_user_password')
}
]
with open(TEST_ACCOUNTS_FILE, "w") as f:
yaml.dump(accounts_list, f, default_flow_style=False)
def configure_tempest_update_params(tempest_conf_file, image_id=None,
flavor_id=None, compute_cnt=1):
"""
Add/update needed parameters into tempest.conf file
"""
logger.debug("Updating selected tempest.conf parameters...")
config = ConfigParser.RawConfigParser()
config.read(tempest_conf_file)
config.set(
'compute',
'fixed_network_name',
CONST.__getattribute__('tempest_private_net_name'))
config.set('compute', 'volume_device_name',
CONST.__getattribute__('tempest_volume_device_name'))
if CONST.__getattribute__('tempest_use_custom_images'):
if image_id is not None:
config.set('compute', 'image_ref', image_id)
if IMAGE_ID_ALT is not None:
config.set('compute', 'image_ref_alt', IMAGE_ID_ALT)
if CONST.__getattribute__('tempest_use_custom_flavors'):
if flavor_id is not None:
config.set('compute', 'flavor_ref', flavor_id)
if FLAVOR_ID_ALT is not None:
config.set('compute', 'flavor_ref_alt', FLAVOR_ID_ALT)
if compute_cnt > 1:
# enable multinode tests
config.set('compute', 'min_compute_nodes', compute_cnt)
config.set('compute-feature-enabled', 'live_migration', True)
config.set('identity', 'region', 'RegionOne')
if os_utils.is_keystone_v3():
auth_version = 'v3'
else:
auth_version = 'v2'
config.set('identity', 'auth_version', auth_version)
config.set(
'validation', 'ssh_timeout',
CONST.__getattribute__('tempest_validation_ssh_timeout'))
config.set('object-storage', 'operator_role',
CONST.__getattribute__('tempest_object_storage_operator_role'))
if CONST.__getattribute__('OS_ENDPOINT_TYPE') is not None:
sections = config.sections()
if os_utils.is_keystone_v3():
config.set('identity', 'v3_endpoint_type',
CONST.__getattribute__('OS_ENDPOINT_TYPE'))
if 'identity-feature-enabled' not in sections:
config.add_section('identity-feature-enabled')
config.set('identity-feature-enabled', 'api_v2', False)
config.set('identity-feature-enabled', 'api_v2_admin', False)
services_list = ['compute',
'volume',
'image',
'network',
'data-processing',
'object-storage',
'orchestration']
for service in services_list:
if service not in sections:
config.add_section(service)
config.set(service, 'endpoint_type',
CONST.__getattribute__('OS_ENDPOINT_TYPE'))
logger.debug('Add/Update required params defined in tempest_conf.yaml '
'into tempest.conf file')
with open(TEMPEST_CONF_YAML) as f:
conf_yaml = yaml.safe_load(f)
if conf_yaml:
sections = config.sections()
for section in conf_yaml:
if section not in sections:
config.add_section(section)
sub_conf = conf_yaml.get(section)
for key, value in sub_conf.items():
config.set(section, key, value)
with open(tempest_conf_file, 'wb') as config_file:
config.write(config_file)
backup_tempest_config(tempest_conf_file)
def configure_verifier(deployment_dir):
"""
Execute rally verify configure-verifier, which generates tempest.conf
"""
tempest_conf_file = os.path.join(deployment_dir, "tempest.conf")
if os.path.isfile(tempest_conf_file):
logger.debug("Verifier is already configured.")
logger.debug("Reconfiguring the current verifier...")
cmd = "rally verify configure-verifier --reconfigure"
else:
logger.info("Configuring the verifier...")
cmd = "rally verify configure-verifier"
ft_utils.execute_command(cmd)
logger.debug("Looking for tempest.conf file...")
if not os.path.isfile(tempest_conf_file):
logger.error("Tempest configuration file %s NOT found."
% tempest_conf_file)
raise Exception("Tempest configuration file %s NOT found."
% tempest_conf_file)
else:
return tempest_conf_file
|
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] |
test/unit2/usersignup/validation_test.py
|
cdoremus/udacity-python_web_development-cs253
| 0 |
166515
|
<reponame>cdoremus/udacity-python_web_development-cs253<gh_stars>0
'''
Created on Apr 25, 2012
@author: h87966
'''
import unittest
from unit2.usersignup.validation import UserSignupValidation
from unit2.usersignup.validation import VERIFICATION_MESSAGES
from unit2.usersignup.validation import VERIFICATION_MESSAGES_KEYS
from unit2.usersignup.validation import MISMATCHED_PASSWORDS_MESSAGE
class Test(unittest.TestCase):
def setUp(self):
self.validation = UserSignupValidation()
pass
def tearDown(self):
pass
def testIsValidUsername(self):
self.assertTrue(self.validation.is_valid_username("Crag"))
self.assertTrue(self.validation.is_valid_username("Crag-Doremus"))
self.assertTrue(self.validation.is_valid_username("Crag_Doremus"))
self.assertTrue(self.validation.is_valid_username("Cra"))
self.assertFalse(self.validation.is_valid_username("ca"))
self.assertFalse(self.validation.is_valid_username("cat!"))
self.assertTrue(self.validation.is_valid_username("abcdefghijklmnopqrst"))
self.assertFalse(self.validation.is_valid_username("abcdefghijklmnopqrstu"))
pass
def testIsValidPassword(self):
self.assertTrue(self.validation.is_valid_password("<PASSWORD>"))
self.assertTrue(self.validation.is_valid_password("<PASSWORD>"))
self.assertFalse(self.validation.is_valid_password("<PASSWORD>"))
pass
def testIsValidEmail(self):
self.assertTrue(self.validation.is_valid_email("<EMAIL>"))
self.assertTrue(self.validation.is_valid_email("<EMAIL>"))
self.assertFalse(self.validation.is_valid_email("Craigfoocom"))
pass
def testValid(self):
username = "Craig"
password = "<PASSWORD>"
verify = "<PASSWORD>"
email = "<EMAIL>"
validMsgs, isValid = self.validation.validate(username, password, verify, email)
self.assertTrue(isValid)
self.assertEmptyMessage([VERIFICATION_MESSAGES_KEYS[0],VERIFICATION_MESSAGES_KEYS[1],VERIFICATION_MESSAGES_KEYS[2],VERIFICATION_MESSAGES_KEYS[3]], validMsgs)
def testValid_BadUsername(self):
username = "Craigasdfasdfasdfasdfadfadfs"
password = "<PASSWORD>"
verify = "craig1"
email = "<EMAIL>"
validMsgs, isValid = self.validation.validate(username, password, verify, email)
self.assertFalse(isValid)
self.assertEquals(VERIFICATION_MESSAGES[VERIFICATION_MESSAGES_KEYS[0]], validMsgs[VERIFICATION_MESSAGES_KEYS[0]])
self.assertEmptyMessage([VERIFICATION_MESSAGES_KEYS[1],VERIFICATION_MESSAGES_KEYS[2],VERIFICATION_MESSAGES_KEYS[3]], validMsgs)
def testValid_BadPassword(self):
username = "Craig"
password = "c1"
verify = "c1"
email = "<EMAIL>"
validMsgs, isValid = self.validation.validate(username, password, verify, email)
self.assertFalse(isValid)
self.assertEquals(VERIFICATION_MESSAGES[VERIFICATION_MESSAGES_KEYS[1]], validMsgs[VERIFICATION_MESSAGES_KEYS[1]])
self.assertEquals(VERIFICATION_MESSAGES[VERIFICATION_MESSAGES_KEYS[2]], validMsgs[VERIFICATION_MESSAGES_KEYS[2]])
self.assertEmptyMessage([VERIFICATION_MESSAGES_KEYS[0],VERIFICATION_MESSAGES_KEYS[3]], validMsgs)
# def testValid_BadVerifyPassword(self):
# username = "Craig"
# password = "c1"
# verify = "c1"
# email = "<EMAIL>"
# validMsgs, isValid = self.validation.validate(username, password, verify, email)
# self.assertFalse(isValid)
# self.assertEquals(VERIFICATION_MESSAGES[VERIFICATION_MESSAGES_KEYS[2]], validMsgs[VERIFICATION_MESSAGES_KEYS[2]])
# self.assertEmptyMessage([VERIFICATION_MESSAGES_KEYS[0],VERIFICATION_MESSAGES_KEYS[1],VERIFICATION_MESSAGES_KEYS[3]], validMsgs)
def testValid_BadEmail(self):
username = "Craig"
password = "<PASSWORD>"
verify = "craig1"
email = "c<EMAIL>"
validMsgs, isValid = self.validation.validate(username, password, verify, email)
self.assertFalse(isValid)
self.assertEquals(VERIFICATION_MESSAGES[VERIFICATION_MESSAGES_KEYS[3]], validMsgs[VERIFICATION_MESSAGES_KEYS[3]])
self.assertEmptyMessage([VERIFICATION_MESSAGES_KEYS[0],VERIFICATION_MESSAGES_KEYS[2],VERIFICATION_MESSAGES_KEYS[1]], validMsgs)
def testValid_PasswordsDontMatch(self):
username = "Craig"
password = "<PASSWORD>"
verify = "craig"
email = "<EMAIL>"
validMsgs, isValid = self.validation.validate(username, password, verify, email)
self.assertFalse(isValid)
self.assertEquals(MISMATCHED_PASSWORDS_MESSAGE, validMsgs[VERIFICATION_MESSAGES_KEYS[1]])
self.assertEquals(MISMATCHED_PASSWORDS_MESSAGE, validMsgs[VERIFICATION_MESSAGES_KEYS[2]])
self.assertEmptyMessage([VERIFICATION_MESSAGES_KEYS[0],VERIFICATION_MESSAGES_KEYS[3]], validMsgs)
def test_is_password_and_verify_equals(self):
self.assertTrue(self.validation.is_password_and_verify_equals("craig", "<PASSWORD>"))
self.assertFalse(self.validation.is_password_and_verify_equals("craig", "<PASSWORD>"))
def assertEmptyMessage(self, key_list, messages):
for key in key_list:
self.assertEquals('', messages[key], "Message with key " + key + " is not empty")
if __name__ == "__main__":
#import sys;sys.argv = ['', 'Test.testValidateUsername']
unittest.main()
|
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2
] |
user/views/contact.py
|
fsr/course-management
| 11 |
75282
|
<filename>user/views/contact.py<gh_stars>10-100
from django.contrib.auth.decorators import login_required
from django.contrib.auth.models import User
from django.http import HttpRequest
from django.shortcuts import render, redirect
from django.views.decorators.csrf import csrf_protect
from django.utils.translation import ugettext as _
from user.forms import ContactForm
from util.error.reporting import db_error
CONTACT_FOOTER = """
-------------------
This message has been sent via the Course Mangement System.
Sent by: """
@login_required
@csrf_protect
def contact_form(request: HttpRequest, user_id: int):
try:
user = User.objects.get(id=user_id)
except User.DoesNotExist:
return db_error(_('Requested user does not exist.'))
assert isinstance(user, User)
if request.method == 'POST':
form = ContactForm(request.POST)
if form.is_valid():
content = form.content + CONTACT_FOOTER + request.user.email
user.email_user(
subject="[CM contact form] " + form.subject,
message=content
)
return redirect('index')
else:
form = ContactForm()
return render(
request,
'contact-form.html',
{
'form': form,
'user': user
}
)
|
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500,
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2
] |
map.py
|
CaptainYin/ParLTRANS
| 0 |
149510
|
<reponame>CaptainYin/ParLTRANS<gh_stars>0
import os
import csv
import numpy as np
import replace
#split iniloc file
parf='IPL_20130913_4590_eddy2'
parent_dir='Model6.5'
jobscript_prefix='6_5'
MapNum=96
parfpath='../loc/wbc20/'+parf+'.csv'
loc=np.loadtxt(open(parfpath,'rb'),delimiter=',',usecols=(0,1,2,3))
offset=loc.shape[0]/MapNum
res=loc.shape[0]%MapNum
num=np.zeros((MapNum,),dtype=np.int)
for i in range(0,MapNum):
parf0=parf+'_'+str(i)
parfpath0='../loc/wbc20/'+parf0+'.csv'
if(i<res):
np.savetxt(parfpath0,loc[i*(offset+1):(i+1)*(offset+1),:],fmt='%f',delimiter=',')
num[i]=loc[i*(offset+1):(i+1)*(offset+1),:].shape[0]
else:
np.savetxt(parfpath0,loc[i*offset+res:(i+1)*offset+res,:],fmt='%f',delimiter=',')
num[i]=loc[i*offset+res:(i+1)*offset+res,:].shape[0]
'''
if(i==MapNum-1):
np.savetxt(parfpath0,loc[i*offset:,:],fmt='%f',delimiter=',')
num[i]=loc[i*offset:,:].shape[0]
# print(loc[i*offset:-1,:].shape[0],'sdfg')
else:
np.savetxt(parfpath0,loc[i*offset:(i+1)*offset,:],fmt='%f',delimiter=',')
num[i]=loc[i*offset:(i+1)*offset,:].shape[0]
#print(loc[i*offset:(i+1)*offset,:].shape[0])
'''
print(num,num.sum())
#create working directory
os.system("rm "+parent_dir+"/output/*")
for i in range(0,MapNum):
os.system("rm "+parent_dir+"."+str(i)+" -r")
for i in range(0,MapNum):
os.system("cp "+parent_dir+" "+parent_dir+"."+str(i)+" -r")
os.system("mv "+parent_dir+"."+str(i)+"/"+jobscript_prefix+" "+parent_dir+"."+str(i)+"/"+jobscript_prefix+"."+str(i))
parameterfile=parent_dir+"."+str(i)+"/LTRANS.data"
name="parfile"
value='\'../../loc/wbc20/'+parf+'_'+str(i)+'.csv\''
replace.setparameter(parameterfile,name,value)
replace.setparameter(parameterfile,"Numpar",str(num[i]))
#run
#for i in range(0,MapNum):
#os.system("cd /vol6/home/zhangxy_zj/YinHQ/LTRANSv.2b-master/"+parent_dir+"."+str(i))
#os.system("pwd")
# os.system("yhbatch -N 1 -n 1 -p weather "+jobscript_prefix+"."+str(i))
'''
#list=os.listdir(root)
#list.sort()
#for i in range(1,9):
# print(i)
# os.system("ncks -d ocean_time,0,,12 scs10_his_000"+str(i)+".nc ../12hour/scs10_his_000"+str(i)+".nc")
#path=os.path.join(root,list[i])
#if os.path.isdir(path):
# os.system("mv "+path+"/scs10_his_0001.nc ./scs10_his_00"+str(i+1).zfill(2)+".nc")
# os.system("mv "+path+"/scs10_his_0002.nc ../halfhour/scs10_his_00"+str(i*4+2).zfill(2)+".nc")
# os.system("mv "+path+"/scs10_his_0003.nc ../halfhour/scs10_his_00"+str(i*4+3).zfill(2)+".nc")
# os.system("mv "+path+"/scs10_his_0004.nc ../halfhour/scs10_his_00"+str(i*4+4).zfill(2)+".nc")
#print path,i
#os.system("cp "+path+"/ocean_scs10_cur_daily.in "+path+"/ocean_scs10_cur.in")
#os.system("ls "+path)
with open(path+"/ocean_scs10_cur.in",'r') as f:
lines=f.readlines()
with open(path+"/ocean_scs10_cur.in",'w') as f_w:
for line in lines:
if "NHIS == 240" in line:
line=" NHIS == 5\n"
if "NDEFHIS == 960" in line:
line=" NDEFHIS == 240\n"
f_w.write(line.encode('utf-8'))
'''
|
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spar_python/analytics/ta2/circuit_parameters/main.py
|
nathanawmk/SPARTA
| 37 |
111964
|
<filename>spar_python/analytics/ta2/circuit_parameters/main.py
# *****************************************************************
# Copyright 2015 MIT Lincoln Laboratory
# Project: SPAR
# Authors: Yang
# Description: Script to add additional parameters to Stealth
# circuit descriptions.
#
# Modifications:
# Date Name Modification
# ---- ---- ------------
# 24 Mar 2013 Yang Original Version
# *****************************************************************
import argparse
from itertools import *
from circuit_parameters import *
def main():
parser = argparse.ArgumentParser(description = 'Appends additional '
'parameters to the first line of each Stealth circuit description '
'in the specified directory')
parser.add_argument('-p, --path', dest = 'path', required = True,
help = 'path to circuit')
args = parser.parse_args();
(num_xor, num_other, fn_xor, fn_other) = parse_circuit(args.path)
print "Number of XOR: {}\nNumber of Other: {}\nXOR fan-in: {}\nOther " \
"fan-in: {}".format(num_xor, num_other, fn_xor, fn_other)
write_parameters(args.path, num_xor, num_other, fn_xor, fn_other)
if __name__ == '__main__':
main()
|
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] |
tests/test_fast_pot.py
|
mudigonda/hmc_scripts
| 0 |
193265
|
from mjhmc.search.objective import obj_func
from mjhmc.samplers.markov_jump_hmc import MarkovJumpHMC
from mjhmc.misc.distributions import ProductOfT
from mjhmc.misc.autocor import autocorrelation
from mjhmc.misc.autocor import sample_to_df
from mjhmc.fast import hmc
from scipy.sparse import rand
import numpy as np
import theano.tensor as T
import matplotlib.pyplot as plt
np.random.seed(2016)
nbasis=36
ndims=12
n_steps=1000
half_window=False
rand_val = rand(ndims,nbasis/2,density=0.1)
W = np.concatenate([rand_val.toarray(), -rand_val.toarray()],axis=1)
logalpha = np.random.randn(nbasis,1)
PoT_instance = ProductOfT(nbatch=100,ndims=ndims,nbasis=nbasis,W=W,logalpha=logalpha)
simulate = hmc.wrapper_hmc(s_rng=s_rng,energy_fn=rw.E_val,dim=dim)
a = np.zeros([1,10,n_samples])
b = np.zeros([2,10,n_samples])
for ii in np.arange(n_steps):
a[:,:,ii],b[:,:,ii] = simulate()
print "Acceptance Rate"
print a[:,:,ii]
print "Samples"
print b[:,:,ii]
ac_df = autocorrelation(df,half_window)
#Now, we can run the same autocorrelation from the data we will extract from the data frame
#but with the theano function
ac= hmc.normed_autocorrelation(df)
#We can compare the two plots individually and on a single plot
#Drop Mic and leave.
n_grad_evals = ac_df['num grad'].astype(int)
|
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1300,
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272,
23445,
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2176,
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13,
29937,
4806,
508,
7252,
278,
1023,
24580,
29689,
322,
373,
263,
2323,
6492,
13,
13,
29937,
15063,
20279,
322,
5967,
29889,
13,
29876,
29918,
5105,
29918,
14513,
29879,
353,
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29918,
2176,
1839,
1949,
4656,
13359,
579,
668,
29898,
524,
29897,
13,
2
] |
orthnet/poly/__init__.py
|
Orcuslc/OrthNet
| 32 |
156506
|
from ._legendre import Legendre, Legendre_Normalized
from ._laguerre import Laguerre
from ._hermite import Hermite, Hermite2
from ._chebyshev import Chebyshev, Chebyshev2
from ._jacobi import Jacobi
__all__ = ['Legendre', 'Legendre_Normalized', 'Laguerre', 'Hermite', 'Hermite2', 'Chebyshev', 'Chebyshev2', 'Jacobi']
|
[
1,
515,
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24288,
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2033,
2
] |
gzdice/rollers/FateRoller.py
|
gleeblezoid/Diceroller
| 1 |
174934
|
# Roller for FATE dice
import random
from ..menus import base_menus as m
def fateroller():
pool = int(input("How many dice are you rolling? "))
rollagain = "Y"
while str.upper(rollagain) == "Y":
try:
sides = 6
if pool < 1:
print("Silly human")
# Dice roll resolution
i = 0
while i < (pool):
roll = random.randint(1, sides)
i = i + 1
if roll == (1 or 2):
print("[+]", end=",")
elif roll == (3 or 4):
print("[-]", end=",")
else:
print("[ ]", end=",")
rollagain = input(
"Do you want to roll the same again?\
Enter Y or N: "
)
except (NameError, TypeError, ValueError):
m.main_menu()
else:
m.main_menu()
|
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29901,
13,
4706,
286,
29889,
3396,
29918,
6510,
580,
13,
2
] |
src/user/migrations/0018_delete_emailpreference.py
|
ResearchHub/ResearchHub-Backend-Open
| 18 |
91565
|
<reponame>ResearchHub/ResearchHub-Backend-Open
# Generated by Django 2.2.8 on 2019-12-11 22:30
from django.db import migrations
class Migration(migrations.Migration):
dependencies = [
('user', '0017_emailpreference'),
]
operations = [
migrations.DeleteModel(
name='EmailPreference',
),
]
|
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529,
276,
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2
] |
airflow_server/dags/cbs.py
|
carmelp16/anyway-etl
| 0 |
163927
|
<reponame>carmelp16/anyway-etl
from airflow import DAG
from airflow.utils.dates import days_ago
from anyway_etl_airflow.operators.cli_bash_operator import CliBashOperator
dag_kwargs = dict(
default_args={
'owner': 'airflow',
},
schedule_interval='@daily',
catchup=False,
start_date=days_ago(2),
)
with DAG('cbs', **dag_kwargs) as cbs_dag:
CliBashOperator(
'anyway-etl cbs import-emails', task_id='import-emails'
) >> CliBashOperator(
'anyway-etl cbs process-files', task_id='process-files'
) >> [
# for local development you can use the following command to parse all types sequentially:
# anyway-etl cbs parse-all
CliBashOperator('anyway-etl cbs parse-accidents', task_id='parse-accidents'),
CliBashOperator('anyway-etl cbs parse-involved', task_id='parse-involved'),
CliBashOperator('anyway-etl cbs parse-vehicles', task_id='parse-vehicles'),
] >> CliBashOperator(
'anyway-etl cbs import-to-datastore', task_id='import-to-datastore'
)
|
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5215,
29899,
517,
29899,
4130,
579,
487,
29915,
13,
1678,
1723,
13,
2
] |
Imaginary/2021/crypto/Textbook_RSA_2_Timmy_the_Lonely_Oracle/oracle.py
|
ruhan-islam/ctf-archives
| 1 |
121833
|
<filename>Imaginary/2021/crypto/Textbook_RSA_2_Timmy_the_Lonely_Oracle/oracle.py
#!/usr/bin/env -S python3 -u
from Crypto.Util.number import *
from hidden import p, q, flag1
e = 65537
n = p*q
ns = str(n)
ctxt = pow(bytes_to_long(flag1), e, n)
class SneakyUserException(Exception):
pass
def print_ctxt(t):
print("Here's the encrypted message:", t)
print("e =", e)
print("n =", ns)
def encrypt():
global e, n, ctxt
ptxt = bytes_to_long(input("What's your message to encrypt? ").encode("utf8"))
print_ctxt(pow(ptxt, e, n))
def decrypt():
global e, p, q, n, ctxt
try:
c = int(input("What's your message to decrypt? "))
if c == ctxt:
raise SneakyUserException
d = pow(e, -1, (p-1)*(q-1))
m = pow(c, d, n)
pt = long_to_bytes(m)
if pt == flag1:
raise SneakyUserException
print("The decrypted message is",m)
print()
try:
print("That spells out \""+pt.decode("utf8")+"\" if that means anything to you.")
except UnicodeDecodeError as u:
print("I couldn't figure out what that says ... are you sure you're doing it correctly?")
except SneakyUserException as e:
print("Hey, that's cheating! You can't ask me to decrypt the flag!")
print("I'm not playing with you any more! Go cheat somewhere else.")
exit()
def menu():
print()
print()
print("1: Get Encrypted Flag")
print("2: Encrypt Message")
print("3: Decrypt Message")
print("4: Quit")
print()
choice = int(input(">>> "))
if choice == 1:
print_ctxt(ctxt)
print()
print("Good luck!")
elif choice == 2:
encrypt()
elif choice == 3:
decrypt()
elif choice == 4:
print("Come back again soon!")
exit()
if __name__ == '__main__':
print("Hi! I'm Timmy! Have you come to play with my encryption system?")
print("Just let me know what I can do for you!")
while True:
try:
menu()
except Exception as ex:
print("Something's gone horribly wrong.")
print("I have to go now. Bye!")
exit()
|
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1,
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29958,
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] |
conan_ue4cli/commands/build.py
|
hobbeshunter/conan-ue4cli
| 0 |
86918
|
import argparse, os, shutil, tempfile
from os.path import basename, exists, join
from ..common import PackageBuilder, ProfileManagement, RecipeCache, Utility
from .update import update
# The default username used when building packages
DEFAULT_USER = 'adamrehn'
def build(manager, argv):
# Our supported command-line arguments
parser = argparse.ArgumentParser(
prog='ue4 conan build',
description = 'Builds Conan packages that depend on conan-ue4cli wrappers'
)
parser.add_argument('--rebuild', action='store_true', help='Rebuild packages that already exist in the local Conan cache')
parser.add_argument('--dry-run', action='store_true', help='Print Conan commands instead of running them')
parser.add_argument('--no-cache', action='store_true', help='Do not add the conan-ue4cli recipe cache to the list of default recipe sources')
parser.add_argument('-s', '-source', action='append', dest='sources', metavar='DIR', help='Add the specified directory as an additional source of buildable package recipes (the only sources available by default are the conan-ue4cli recipe cache and the current working directory)')
parser.add_argument('-o', '-option', action='append', dest='options', metavar='PKG:OPTION=VALUE', help='Specify options to pass to package recipes when building them (does not affect dependency resolution)')
parser.add_argument('-user', default=DEFAULT_USER, help='Set the user for the built packages (default user is "{}")'.format(DEFAULT_USER))
parser.add_argument('-upload', default=None, metavar='REMOTE', help='Upload the built packages to the specified Conan remote')
parser.add_argument('-p', '-profile', dest='profile', metavar='PROFILE', default=None, choices=ProfileManagement.listGeneratedProfiles(), help='Build packages using the specified Conan profile (defaults to the profile for the host platform and most Unreal Engine installation used to perform profile generation)')
parser.add_argument('package', nargs='+', help='Package(s) to build, in either NAME or NAME==VERSION format (specify "all" to build all available packages)')
# Parse the supplied command-line arguments
args = parser.parse_args(argv)
# If a profile was not specified, fallback to the default for the host platform (or using the generic one if the default doesn't exist)
if args.profile is None:
preferredDefault = ProfileManagement.profileForHostPlatform(manager)
genericFallback = ProfileManagement.genericProfile()
if preferredDefault in ProfileManagement.listGeneratedProfiles():
print('Using default profile for host platform "{}"'.format(preferredDefault))
args.profile = preferredDefault
else:
print('Warning: falling back to generic profile "{}" because the profile "{}" does not exist.'.format(genericFallback, preferredDefault))
print('You should run `ue4 conan generate` to generate the host platform profile for the current Engine installation.')
args.profile = genericFallback
# Retrieve the Unreal Engine version string for the specified Conan profile and use it as the channel
channel = ProfileManagement.profileEngineVersion(args.profile)
# Create an auto-deleting temporary directory to hold our aggregated recipe sources
with tempfile.TemporaryDirectory() as tempDir:
# Determine if we are including the recipe cache directory in our list of source directories
cacheDir = RecipeCache.getCacheDirectory()
defaultSources = [os.getcwd()] + ([cacheDir] if args.no_cache == False else [])
# If the recipe cache directory does not exist (usually because this is our first build), then update it
if args.no_cache == False and exists(cacheDir) == False:
update(manager, argv)
# Iterate over our source directories and copy our recipes to the temp directory
sources = defaultSources + (args.sources if args.sources is not None else [])
for source in sources:
for recipe in Utility.listPackagesInDir(source):
try:
shutil.copytree(join(source, recipe), join(tempDir, recipe))
except FileExistsError as e:
conflict = basename(str(e).split(': ')[-1].strip('"\''))
raise RuntimeError('conflicting source directories detected for recipe {}'.format(conflict)) from None
# Create our package builder
builder = PackageBuilder(tempDir, args.user, channel, args.profile, args.rebuild, args.dry_run)
# Process the specified list of packages, resolving versions as needed
packages = []
for arg in args.package:
if arg.lower() == 'all':
packages.extend(list([builder.identifyNewestVersion(p) for p in builder.availablePackages]))
elif '==' in arg:
packages.append(arg.replace('==', '/'))
else:
packages.append(builder.identifyNewestVersion(arg))
# Perform dependency resolution and compute the build order for the packages
buildOrder = builder.computeBuildOrder(packages)
# Verify that we are building at least one package
if len(buildOrder) == 0:
print('No packages need to be built. Use the --rebuild flag to rebuild existing packages.')
return
# Report the computed build order to the user
uploadSuffix = ' and uploaded to the remote "{}"'.format(args.upload) if args.upload is not None else ''
print('\nThe following packages will be built{}:'.format(uploadSuffix))
for package in buildOrder:
print('\t' + package)
# Attempt to build the packages
builder.buildPackages(buildOrder, args.options if args.options is not None else [])
# If a remote has been specified to upload the built packages to, attempt to do so
if args.upload is not None:
builder.uploadPackages(buildOrder, args.upload)
|
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language/bert_extraction/steal_bert_classifier/utils/wiki103_sentencize.py
|
Xtuden-com/language
| 1,199 |
1727
|
<filename>language/bert_extraction/steal_bert_classifier/utils/wiki103_sentencize.py<gh_stars>1000+
# coding=utf-8
# Copyright 2018 The Google AI Language Team Authors.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
# Lint as: python3
"""Sentencize the raw wikitext103."""
import tensorflow.compat.v1 as tf
app = tf.app
flags = tf.flags
gfile = tf.gfile
logging = tf.logging
flags.DEFINE_string("wiki103_raw", None,
"Path to raw wikitext103 train corpus.")
flags.DEFINE_string("output_path", None,
"Path to output the processed dataset.")
FLAGS = flags.FLAGS
def main(_):
with open(FLAGS.wiki103_raw, "r") as f:
data = f.read().strip().split("\n")
data = [x.split(" . ") for x in data if x.strip() and x.strip()[0] != "="]
sentences = []
for para in data:
for sent in para:
sentences.append(sent + ".")
data = "\n".join(sentences)
data = data.replace(" @.@ ", ".").replace(" @-@ ", "-").replace(" ,", ",")
data = data.replace(" \'", "\'").replace(" )", ")").replace("( ", "(")
data = data.replace(" ;", ";")
data = "\n".join([x for x in data.split("\n") if len(x.split()) > 3])
logging.info("length = %d", len(data.split("\n")))
with open(FLAGS.output_path, "w") as f:
f.write(data)
if __name__ == "__main__":
app.run(main)
|
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2
] |
setup.py
|
codingjoe/codingjoes_new_package
| 0 |
192096
|
#!/usr/bin/env python
from setuptools import setup
setup(version_format='{tag}.dev{commits}')
|
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] |
StatusWindow.py
|
BlackXanthus/PyBirch5
| 0 |
35728
|
####
#Made reduntant in Alpha 0.4
#
# As this provided no extra functionality from the Channel, and is, in essance,
# simply a speciall channel, various additions were made to Channel.py
# To perform the same function. This has meant less duplication of code.
####
import sys, sip
from PyQt5 import QtCore, QtGui
#from pybirchMdiV4 import Ui_PyBirch
#from IrcConnection import IrcConnection
#from ServerOutputSorter import ServerOutputSorter
from pybirchMessage import Ui_BirchMessageWindow
from userInputSorter import UserInputSorter
from TextString import TextString
from NickListView import NickListView
from time import strftime
from struct import unpack
import string
try:
from PyQt5.QtCore import QString
except ImportError:
QString = str
class StatusWindow(QtGui.QMdiSubWindow):
_channel = ""
_nick = ""
_button = ""
__version__ = "Status v. 0.1"
def __init__(self, my_channel, parent=None):
self._channel=my_channel
self._nick=""
#Config will need to be passed in later, because of the order of
#creation.
#self.config=my_config
#print "DEBUG-------->In Channel :"+_channel_
QtGui.QMdiSubWindow.__init__(self,parent)
sip.delete(self.layout())
self.ui = Ui_BirchMessageWindow()
self.ui.setupUi(self)
# self.ui.setupUi(self)
self.ui.label_ChanName.setText(self._channel)
self.setWindowTitle(self._channel)
#install the key event filter.
self.ui.text_input.installEventFilter(self)
#self._nicklist = NickListView()
#self.ui.list_NickList.setModel(self._nicklist)
# self.ui.setWindowTitle(_channel_)
#Quick and Dirty hashing method.
#--- May have problems with similarly-named channels. eg
# test, tset, ttse and so on. MUST TEST (v0.1)
#http://bytes.com/topic/python/answers/23620-string-ascii-values
# def __hash__(self):
# global _channel_
# return unpack('%sB' % len(value), value)
def get_channel_name(self):
return self._channel
def append_channel_text(self,myString):
myQString = QString(str(myString))
showTime = "True"
#self.ui.editor_Window.append(myQString)
#try:
# showTime=self.config.get("Channel_Settings", "Time")
#except:
# self.config.add_section("Channel_Settings")
# self.config.set("Channel_Settings","Time", "True")
# showTime="True"
if showTime=="True":
current_time = strftime("%H:%M")
myQString="["+current_time+"] "+myQString
self.ui.editor_Window.insertHtml(myQString+"<br>")
#This should stop the window from updating when scrolling #up and down through the channel!
# if not self.ui.editor_Window.isHorizontalSliderPressed():
self.ui.editor_Window.moveCursor(11,False)
del myQString
def eventFilter(self, obj ,event):
if event.type() == QtCore.QEvent.KeyPress and event.matches(QtGui.QKeySequence.InsertParagraphSeparator):
# self.sendToServer()
self.process_input_event()
if event.type() == QtCore.QEvent.KeyPress and event.key() == QtCore.Qt.Key_Tab:
self.process_tab_event()
return True
return False
def process_tab_event(self):
print("Processing Tab Event")
#originalString = self.ui.text_input.text()
#searchString = str(originalString.rsplit(None, 1))
#searchString = originalString.split(" ")[-1]
#print(searchString)
#resultString = self._nicklist.search_nick(searchString)
#if resultString != "":
#originalString=originalString.rsplit(" ", 1)[0]
#if originalString.endswith(searchString):
# originalString= originalString[:-len(searchString)]
#originalString = originalString.rstrip(searchString)
# if len(originalString) == 0:
# self.ui.text_input.setText(originalString +resultString)
# else:
# self.ui.text_input.setText(originalString +" "+ resultString)
def process_input_event(self):
channelName=self._channel
originalString = self.ui.text_input.text()
textToSend = originalString
displayText = textToSend
uIS = UserInputSorter()
ts = uIS.process_input(channelName, self._nick, originalString)
self.ui.editor_Window.moveCursor(11,False)
#
# myTextToSwitch = textToSend.split(" ")
#
# if myTextToSwitch[0][0:1] == "/":
# if myTextToSwitch[0] == "/msg":
# #Note, this doesn't in any way work.
# remainderIndex = textToSend.find(myTextToSwitch[1])
# textToSend = "PRIVMSG "+myTextToSwitch[1]+" "+textToSend[remainderIndex:]
# displayText = "**Messaging "+myTextToSwitch[1]+textToSend[remainderIndex:]
# else:
# textToSend = str(textToSend[1:])
# displayText = "---"+str(textToSend)
#remainderIndex=string.find(strServerOutput,":",2)
# else:
# textToSend = "PRIVMSG "+channelName+" :"+textToSend
# displayText = "["+_nick_+"] "+originalString
#try:
# showTime=self.config.get("Channel_Settings", "Time")
#except:
# pass
showTime = "True"
self.emit(QtCore.SIGNAL("UserInput"),ts.get_original_string())
myDisplayString = ts.get_display_string()
if showTime == "True":
current_time = strftime("%H:%M")
myDisplayString="["+current_time+"] "+myDisplayString
self.ui.editor_Window.insertHtml(myDisplayString+"<br>")
self.ui.text_input.setText("")
def nick_update(self, my_new_nick):
self._nick = my_new_nick
def closeEvent(self, closeEvent):
self.emit(QtCore.SIGNAL("Channel_Closed"),self._channel)
closeEvent.accept();
print ("<StatusWindow : Close event> PANIC Mr Mannering!")
def button_click(self):
#sender = self.sender
if self.isMinimized():
self.show()
self.showNormal()
else:
self.showMinimized()
self.hide()
###
# While insert_nick currently works, the listWidget will need a QAbstractView in order to be able to remove
# items. This will deal with @, +, and other standard modifiers of the channel. definately TODO!
#
# def insert_nick(self, ts):
# #this is being done this way for future proofing
# print ("<DEBUG>Channel.py:insert_nick", ts.get_message())
# for myNick in ts.get_message().split():
# myNickToAdd = myNick.replace(":","",1 )
# self._nicklist.insert_nick(myNickToAdd)
# self.ui.listWidget.addItem(QtGui.QListWidgetItem(myNickToAdd))
# def remove_nick(self, ts):
# print("<DEBUG>Channel.py:remove_nick"+self._channel+" :"+ts.get_nick())
# for myNick in ts.get_nick().split():
# myNickToRemove = myNick.replace(":", "", 1)
# found = self._nicklist.remove_nick(myNickToRemove)
# self.ui.listWidget.removeItemWidget(QtGui.QListWidgetItem(myNickToRemove))
# if(found):
# self.append_channel_text(ts.get_display_string())
# def nick_mode_change(self, ts):
# self._nicklist.changeStatus(ts.get_mode_user(), ts.get_mode_settings())
|
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29918,
11027,
3101,
13,
2
] |
zerver/lib/timestamp.py
|
Supermanu/zulip
| 0 |
107404
|
<gh_stars>0
from __future__ import absolute_import
import datetime
import calendar
from django.utils.timezone import utc as timezone_utc
def floor_to_hour(dt):
# type: (datetime.datetime) -> datetime.datetime
return datetime.datetime(*dt.timetuple()[:4]) \
.replace(tzinfo=dt.tzinfo)
def floor_to_day(dt):
# type: (datetime.datetime) -> datetime.datetime
return datetime.datetime(*dt.timetuple()[:3]) \
.replace(tzinfo=dt.tzinfo)
def ceiling_to_hour(dt):
# type: (datetime.datetime) -> datetime.datetime
floor = floor_to_hour(dt)
if floor == dt:
return floor
return floor + datetime.timedelta(hours=1)
def ceiling_to_day(dt):
# type: (datetime.datetime) -> datetime.datetime
floor = floor_to_day(dt)
if floor == dt:
return floor
return floor + datetime.timedelta(days=1)
def timestamp_to_datetime(timestamp):
# type: (float) -> datetime.datetime
return datetime.datetime.fromtimestamp(float(timestamp), tz=timezone_utc)
class TimezoneNotUTCException(Exception):
pass
def datetime_to_timestamp(dt):
# type: (datetime.datetime) -> int
if dt.tzinfo is None or dt.tzinfo.utcoffset(dt) != timezone_utc.utcoffset(dt):
raise TimezoneNotUTCException("Datetime %s to be converted does not have a UTC timezone." % (dt,))
return calendar.timegm(dt.timetuple())
|
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5974,
8028,
3664,
26913,
2451,
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16390,
5410,
1273,
29879,
304,
367,
11543,
947,
451,
505,
263,
17998,
29431,
1213,
1273,
313,
6008,
29892,
876,
13,
1678,
736,
17684,
29889,
9346,
387,
29885,
29898,
6008,
29889,
9346,
24120,
552,
3101,
13,
2
] |
download.py
|
reed-hackathon-2022/mc-bot
| 1 |
30722
|
<gh_stars>1-10
import io
from google.oauth2.credentials import Credentials
from googleapiclient.discovery import build
from googleapiclient.http import MediaIoBaseDownload
from google.oauth2 import service_account
from oauth2client.service_account import ServiceAccountCredentials
SCOPES = ['https://www.googleapis.com/auth/drive.metadata.readonly']
def get_drive_service():
creds = Credentials.from_authorized_user_file('googletoken.json')
if not creds or not creds.valid:
creds = ServiceAccountCredentials.from_json_keyfile_dict('serviceaccountcreds.json', scopes=SCOPES)
return build('drive', 'v3', credentials=creds)
def fetch_document():
drive_service = get_drive_service()
file_id = "1s0S4oau1GuJSrzaFhYHfLLq0KRliO4rG2uerNHpXRCk"
request = drive_service.files().export_media(
fileId=file_id, mimeType='text/plain'
)
fh = io.StringIO()
downloader = MediaIoBaseDownload(fh, request)
done = False
while not done:
status, done = downloader.next_chunk()
print(f"Download status: {status :%}%")
drive_service.close()
return fh.getvalue()
|
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] |
src/anim.py
|
JovialKnoll/monsters
| 2 |
18914
|
<gh_stars>1-10
import pygame.mixer
from vec2d import Vec2d
from saveable import Saveable
class Anim(Saveable):
__slots__ = (
'func',
'time',
'pos',
'sound',
'positional_sound',
)
def __init__(self, func: str, time: int, x_or_pair, y=None,
sound: pygame.mixer.Sound = None, positional_sound: bool = False):
self.func = func
self.time = time
self.pos = Vec2d(x_or_pair, y)
self.sound = sound
self.positional_sound = positional_sound
def save(self):
# no sound right now, sorry
# if we need it, either start passing sounds as paths
# or don't save when there are pending Anims
return self.func, self.time, self.pos
@classmethod
def load(cls, save_data):
return cls(*save_data)
|
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7620,
29918,
1272,
29897,
13,
2
] |
sketches/aquarium/aquarium.pyde
|
kantel/processingpy
| 4 |
153845
|
from fish import Fish
WIDTH = 640
HEIGHT = 416
NFISHES = 15 # Anzahl der Fische
FPS = 60
fishes = []
def setup():
global bg
size(WIDTH, HEIGHT)
this.surface.setTitle(u"<NAME>, bonbonbuntes Aquarium")
bg = loadImage("background.png")
for _ in range(NFISHES):
fishes.append(Fish())
frameRate(FPS)
def draw():
background(49, 197, 224) # Himmelblau
image(bg, 0, 0)
for fish in fishes:
fish.show()
fish.update()
|
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] |
array/arrayBasicprogram.py
|
abegpatel/Array-Practice-Programs
| 1 |
50115
|
<filename>array/arrayBasicprogram.py
# -*- coding: utf-8 -*-
"""
Created on Wed Mar 24 22:12:02 2021
@author: Abeg
"""
#all symmetric pair of an array
def symmetricpair(pairs):
s=set()
for (x,y) in pairs:
s.add((x,y))
if (y,x) in s:
print((x,y),"|",((y,x)))
pairs=[(11,20),(30,40),(5,10),(40,30),(10,5)]
print(symmetricpair(pairs),end="")
#repeating element
n=int(input("enter the size"))
arr=[]
for i in range(0,n):
e=int(input())
arr.append(e)
x=list(dict.fromkeys(arr))
for i in x:
if(arr.count(i)>1):
print(i)
#non-repeating element
n=int(input("enter the size"))
arr=[]
for i in range(0,n):
e=int(input())
arr.append(e)
x=list(dict.fromkeys(arr))
for i in x:
if(arr.count(i)==1):
print(i)
#no of even element and odd elemnet in an array
arr=[]
n=int(input())
count1=0
count2=0
for i in range(n):
e=int(input())
arr.append(e)
for i in range(n):
if(arr[i]%2==0):
count1+=1
else:
count2+=1
print("no of even",count1)
print("no of odd is",count2)
#finding min scalarproduct of two vector
arr1=[]
arr2=[]
n=int(input("size of the array"))
for i in range(n):
e=int(input())
arr1.append(e)
for i in range(n):
e=int(input())
arr2.append(e)
list=()
arr1.sort()
arr2.sort(reverse=True)
sum=0
for i in range(0,n):
sum=sum+arr1[i]*arr2[i]
print(sum)
#minimum abs difference of given array
arr=[5,10,1,4,8,7]
n=len(arr)
sum=0
arr.sort()
sum=sum+abs(arr[0]-arr[1])
sum+=abs(arr[n-1]-arr[n-2])
for i in range(1,n-1):
sum+=min(abs(arr[i]-arr[i-1]),abs(arr[i]-arr[i+1]))
print(sum)
#finding max scalarproduct of two vector
arr1=[]
arr2=[]
n=int(input("size of the array"))
for i in range(n):
e=int(input())
arr1.append(e)
for i in range(n):
e=int(input())
arr2.append(e)
list=()
arr1.sort()
arr2.sort()
sum=0
for i in range(0,n):
sum=sum+arr1[i]*arr2[i]
print(sum)
# Function to return maximum product of a sublist of given list
def maxProduct(A):
max_ending = min_ending = 0
max_so_far = 0
for i in A:
temp = max_ending
max_ending = max(i, max(i * max_ending, i * min_ending))
min_ending = min(i, min(i * temp, i * min_ending))
max_so_far = max(max_so_far, max_ending)
return max_so_far
if __name__ == '__main__':
A = [6,-10,-3,0,5]
print("The maximum product of a sublist is", maxProduct(A))
#check frequency of each char
n=int(input("enter the size"))
arr=[]
for i in range(0,n):
e=int(input())
arr.append(e)
x=list(dict.fromkeys(arr))
for i in x:
print("{}occours {} time".format(i,arr.count(i)))
#removing duplicate element prom an array
n=int(input("enter the size"))
arr=[]
for i in range(0,n):
e=int(input())
arr.append(e)
x=list(dict.fromkeys(arr))
print(x)
#distinctelement
n=int(input("enter the size"))
arr=[]
for i in range(0,n):
e=int(input())
arr.append(e)
x=list(dict.fromkeys(arr))
print(x)
print(len(x))
#ARR is disjoint or not
def disjoint(arr1,arr2):
for i in range(0,len(arr1)):
for j in range(0,len(arr2)):
if(arr1[i]==arr2[j]):
return -1
else:
return 1
arr1=[1,2,3,4]
arr2=[1,2,3,4]
res=disjoint(arr1,arr2)
print(res)
if(res==-1):
print("not disjoint")
else:
print("disjoint")
#dtermine array is a subset of anthor array or not
def subset(arr1,arr2,m,n):
i=0
j=0
for i in range(n):
for j in range(m):
if(arr2[i]==arr1[j]):
break
if(j==m):
return 0
return 1
arr1=[1,2,3,4]
arr2=[2,3]
m=len(arr1)
n=len(arr2)
if(subset(arr1,arr2,m,n)):
print("arr2 is a subset of arr1")
else:
print("not")
#rotation from left to right
arr=[10,20,30,40,50]
k=int(input("enter the no of rotation"))
k=k%len(arr)
for i in range(k):
x=arr.pop(-1)
arr.insert(0,x)
print(arr)
#all no array be made equal
def make_equal(arr,n):
for i in range(n):
while(arr[i]%2==0):
arr[i]=arr[i]/2
while(arr[i]%3==0):
arr[i]=arr[i]/3
for i in range(n):
if arr[i]!=arr[0]:
return False
else:
return True
arr=[3,50,75,100]
n=len(arr)
if(make_equal(arr,n)):
print("yes")
else:
print("no")
#sum of dight in the array
def sumarr(arr,n):
for i in range(n):
temp=arr[i]
sum=0
while(temp>0):
d=temp%10
sum=sum+d
temp=temp//10
print(sum,end=" ",sep=" ")
arr=[]
n=int(input("size"))
for i in range(n):
e=int(input())
arr.append(e)
sumarr(arr,n)
#placed all even no followed odd no
# variables
def rearrangeEvenAndOdd(arr, n) :
j = -1
for i in range(0, n) :
if (arr[i] % 2 == 0) :
j = j + 1
temp = arr[i]
arr[i] = arr[j]
arr[j] = temp
n=int(input("fj"))
arr=[]
for i in range(n):
arr.append(int(input()))
rearrangeEvenAndOdd(arr, n)
for i in range(n):
print(arr[i],end=" ",sep=" ")
#array rotation
def leftrotate(arr,d,n):
for i in range(d):
leftrotateone(arr,n)
def leftrotateone(arr,n):
temp=arr[0]
for i in range(n-1):
arr[i]=arr[i+1]
arr[n-1]=temp
arr=[1,2,3,4,5,6]
print(arr)
leftrotate(arr,2,len(arr))
print(arr)
#max Element in an array
def largeelearray(arr):
max=arr[0]
for i in range(0,len(arr)):
if arr[i]>max:
max=arr[i]
else:
max=max
return max
arr=[1,2,3,4]
print(largeelearray(arr))
#recurrent element in an array
def recurr(arr,n):
for i in range(0,n):
for j in range(0,n):
if arr[i]==arr[j+1]:
return arr[i]
else:
return False
arr=[2,5,1,2,3,5,1,2,4]
n=len(arr)
print(recurr(arr,n))
#O(n^2)
#o(1)
#sum of array
def sumarray(arr):
sum=0
for i in range(0,len(arr)):
sum=sum+arr[i]
return sum
arr=[1,2,3,4]
print(sumarray(arr))
#Twosum array
"""
def twosum(arr,target):
outputarr=[]
for i in range(0,len(arr),1):
for j in range(i+1,len(arr),1):
if (arr[j] == target - arr[i]):
outputarr.append(i)
outputarr.append(j)
return outputarr
target=int(input("uju"))
arr=[2,3,4,5]
print(twosum(arr,target))"""
"""
def twosum(arr,target):
outputarr=[]
for i in range(0,len(arr)):
for j in range(i+1,len(arr)):
sum=arr[i]+arr[j]
if sum==target:
outputarr.append(i)
outputarr.append(j)
return outputarr
print(twosum(arr=[2,7,11,15],target=9))"""
|
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] |
models/nlp/albert/run_pretraining.py
|
indhub/deep-learning-models
| 0 |
128252
|
"""
albert-base
wrapped_train_step w/ 32bsz, 1acc: 3.13 it/s; 3.51 it/s
wrapped train_step w/ skipping loss/acc allreduce: ? it/s, 3.54 it/s
wrapped train_step w/ skipping xla: ? it/s, 1.50 it/s
wrapped train_batch & wrapped_allreduce: 2.99 it/s, 3.42 it/s
Max per-GPU batch size (albert):
base:
- 512seq: 32 on p3dn w/ 2 grad_acc (1.72 it/s on TF2.1, 1.82 it/s single-node), 16 on p316
large:
- 512seq: 16 on p3dn w/ 4 grad_acc (0.60 it/s on TF2.1, 0.63 it/s single-node), 8 on p316
Max per-GPU batch size (bert):
base:
- 512seq: 16 on p3dn w/ 4 grad_acc (0.91 it/s on TF 2.1, 1.31 it/s single-node)
large:
- 512seq: 8 on p3dn w/ 8 grad_acc (0.31 it/s on TF 2.1, 0.47 it/s single-node)
A training run of 125k steps is 125k/(1.72 * 3600) ~= 20 hours for base trained on 512seq.
"""
import argparse
import datetime
import glob
import logging
import math
import os
from tempfile import TemporaryDirectory
from typing import List
import tensorflow as tf
import tqdm
from run_squad import run_squad_and_get_results
from tensorflow_addons.optimizers import LAMB, AdamW
from transformers import ( # GradientAccumulator,
AutoConfig,
TFAlbertForMaskedLM,
TFAlbertModel,
TFAutoModelForPreTraining,
TFBertForPreTraining,
)
from gradient_accumulator import GradientAccumulator
from learning_rate_schedules import LinearWarmupLinearDecaySchedule
from utils import get_tokenizer, rewrap_tf_function
# See https://github.com/huggingface/transformers/issues/3782; this import must come last
import horovod.tensorflow as hvd # isort:skip
logger = logging.getLogger(__name__)
def gather_indexes(sequence_tensor: "[batch,seq_length,width]", positions) -> tf.Tensor:
"""Gathers the vectors at the specific positions over a minibatch."""
sequence_shape = sequence_tensor.shape.as_list()
batch_size = sequence_shape[0]
seq_length = sequence_shape[1]
width = sequence_shape[2]
flat_offsets = tf.reshape(tf.range(0, batch_size, dtype=tf.int64) * seq_length, [-1, 1])
flat_positions = tf.reshape(positions + flat_offsets, [-1])
flat_sequence_tensor = tf.reshape(sequence_tensor, [batch_size * seq_length, width])
output_tensor = tf.gather(flat_sequence_tensor, flat_positions)
output_tensor = tf.reshape(output_tensor, [batch_size, -1, width])
return output_tensor
def gather_indexes_2d(sequence_tensor: "[batch,seq_length,width]", positions) -> tf.Tensor:
sequence_shape = sequence_tensor.shape.as_list()
batch_size = sequence_shape[0]
seq_length = sequence_shape[1]
flat_offsets = tf.reshape(tf.range(0, batch_size, dtype=tf.int64) * seq_length, [-1, 1])
flat_positions = tf.reshape(positions + flat_offsets, [-1])
flat_sequence_tensor = tf.reshape(sequence_tensor, [batch_size * seq_length])
output_tensor = tf.gather(flat_sequence_tensor, flat_positions)
output_tensor = tf.reshape(output_tensor, [batch_size, -1])
return output_tensor
def get_mlm_dataset(
*,
filenames: List[str],
max_seq_length: int,
max_predictions_per_seq: int,
batch_size: int,
buffer_size: int = 1000,
) -> tf.data.Dataset:
""" Reads the dataset from TFRecords and returns it. """
def _parse_function(example_proto):
# Parse the input `tf.Example` proto using the dictionary above.
return tf.io.parse_single_example(example_proto, name_to_features)
name_to_features = {
"input_ids": tf.io.FixedLenFeature([max_seq_length], tf.int64), # corresponds to input_ids
"input_mask": tf.io.FixedLenFeature(
[max_seq_length], tf.int64
), # corresponds to attention_mask
"segment_ids": tf.io.FixedLenFeature(
[max_seq_length], tf.int64
), # corresponds to token_type_ids
"masked_lm_positions": tf.io.FixedLenFeature(
[max_predictions_per_seq], tf.int64
), # The number in the sequence that is masked, in range [0, max_seq_length]. 0 signifies a pad.
"masked_lm_ids": tf.io.FixedLenFeature(
[max_predictions_per_seq], tf.int64
), # The token id that is masked, in range [0, vocab_size]. 0 signifies a pad.
"masked_lm_weights": tf.io.FixedLenFeature(
[max_predictions_per_seq], tf.float32
), # 1 if useful, 0 signifies a pad token
"next_sentence_labels": tf.io.FixedLenFeature([1], tf.int64),
}
# Example input pipeline here: https://github.com/NVIDIA/DeepLearningExamples/blob/master/TensorFlow/LanguageModeling/BERT/run_pretraining.py#L443
# 2048 TFRecord files here
if hvd.rank() == 0:
logger.info(f"Dataset length {len(filenames)}")
logger.debug(filenames)
assert len(filenames) > 0, f"Filenames is an empty list"
# Shard and shuffle the filenames
dataset = tf.data.Dataset.from_tensor_slices(filenames)
dataset = dataset.shard(hvd.size(), hvd.rank())
dataset = dataset.shuffle(buffer_size=len(filenames), reshuffle_each_iteration=True)
dataset = dataset.repeat()
# `cycle_length` is the number of parallel files that get read
num_cpu_threads = 2 * 96
cycle_length = min(num_cpu_threads, len(filenames))
# file_to_dataset_func = lambda file: tf.data.TFRecordDataset(file).map(_parse_function)
file_to_dataset_func = lambda file: tf.data.TFRecordDataset(file)
dataset = dataset.interleave(
file_to_dataset_func,
cycle_length=cycle_length,
block_length=1,
num_parallel_calls=cycle_length,
)
# Map and batch will be automatically fused together, see https://www.tensorflow.org/api_docs/python/tf/data/experimental/map_and_batch
dataset = dataset.map(_parse_function, num_parallel_calls=num_cpu_threads)
dataset = dataset.shuffle(buffer_size=buffer_size, reshuffle_each_iteration=True)
dataset = dataset.batch(batch_size, drop_remainder=True)
# Shuffle the batches and prefetch some batches
dataset = dataset.shuffle(buffer_size=buffer_size, reshuffle_each_iteration=True)
return dataset
def mlm_loss_fn(
prediction_logits: "[batch, max_seq_len (512), vocab_size]",
label_positions: "[batch, num_masks (20)]",
label_ids: "[batch, num_masks (20)]",
label_weights: "[batch, num_masks (20)]",
):
""" label_weights is either 1 or 0, 0 meaning masked. """
logits_at_positions = gather_indexes(
prediction_logits, label_positions
) # [b, num_masks, vocab_size]
preds_at_positions = tf.math.argmax(logits_at_positions, -1) # [b, num_masks]
denominator = tf.reduce_sum(label_weights) + 1e-5
cross_entropy_per_token = label_weights * tf.nn.sparse_softmax_cross_entropy_with_logits(
labels=label_ids, logits=logits_at_positions
) # [b, num_masks]
cross_entropy_numerator = tf.reduce_sum(cross_entropy_per_token) # [1]
accuracy_per_token = label_weights * tf.cast(
tf.math.equal(label_ids, preds_at_positions), tf.float32
) # [b, num_tokens]
accuracy_numerator = tf.reduce_sum(accuracy_per_token) # [1]
cross_entropy = cross_entropy_numerator / denominator
accuracy = accuracy_numerator / denominator
return cross_entropy, accuracy
def sop_loss_fn(
prediction_logits: "[b, 2]", next_sentence_labels: "[b, 1]",
):
""" Note that this works for either NSP or SOP, the difference is in how data is generated.
We want to use SOP objective.
"""
label_truth = tf.squeeze(next_sentence_labels) # [b]
label_preds = tf.math.argmax(prediction_logits, -1) # [b]
cross_entropy_batch = tf.nn.sparse_softmax_cross_entropy_with_logits(
labels=label_truth, logits=prediction_logits
) # [b]
accuracy_batch = tf.cast(tf.math.equal(label_truth, label_preds), tf.float16) # [b]
cross_entropy = tf.reduce_mean(cross_entropy_batch) # [1]
accuracy = tf.reduce_mean(accuracy_batch) # [b]
return cross_entropy, accuracy
def train_batch(
*,
model,
opt,
gradient_accumulator,
input_ids,
attention_mask,
token_type_ids,
label_positions,
label_ids,
label_weights,
next_sentence_labels,
skip_sop: bool,
skip_mlm: bool,
):
with tf.GradientTape() as tape:
input_dict = {
"input_ids": input_ids,
"attention_mask": attention_mask,
"token_type_ids": token_type_ids,
}
mlm_logits, sop_logits, hidden_states = model(input_dict, training=True)
# MLM calculation
if skip_mlm:
mlm_loss, mlm_acc = tf.constant(0.0), tf.constant(0.0)
else:
mlm_loss, mlm_acc = mlm_loss_fn(
prediction_logits=mlm_logits,
label_positions=label_positions,
label_ids=label_ids,
label_weights=label_weights,
)
# SOP calculation
if skip_sop:
sop_loss, sop_acc = tf.constant(0.0), tf.constant(0.0)
else:
sop_loss, sop_acc = sop_loss_fn(
prediction_logits=sop_logits, next_sentence_labels=next_sentence_labels
)
loss = tf.cast(mlm_loss, dtype=tf.float32) + tf.cast(
sop_loss, dtype=tf.float32
) # Should there be a coefficient on one of these?
scaled_loss = opt.get_scaled_loss(loss)
scaled_grads = tape.gradient(scaled_loss, model.trainable_variables)
step_grads = opt.get_unscaled_gradients(scaled_grads)
gradient_accumulator(step_grads)
return loss, mlm_loss, mlm_acc, sop_loss, sop_acc
def allreduce(model, opt, gradient_accumulator, loss, mlm_loss, mlm_acc, sop_loss, sop_acc):
grads = gradient_accumulator.gradients
# This, which is equivalent to sparse_as_dense=True, gives a mild 2% speedup from 0.62 it/s to 0.63 it/s
# onn BERT-large multinode.
grads = [
tf.convert_to_tensor(grad)
if grad is not None and isinstance(grad, tf.IndexedSlices)
else grad
for grad in grads
]
# TODO: Does placing this clip before or after allreduce affect accuracy?
# Placing before has a regularization effect, no single example can contribute as much.
# Placing before also gives a 20% speedup when training BERT-large, probably because the
# gradient operations can be fused by XLA.
(grads, grad_norm) = tf.clip_by_global_norm(grads, clip_norm=args.max_grad_norm)
grads = [
hvd.allreduce(grad, compression=hvd.Compression.fp16) if grad is not None else None
for grad in grads
]
opt.apply_gradients(
[(grad, var) for (grad, var) in zip(grads, model.trainable_variables) if grad is not None]
)
# Clear the gradient accumulator
gradient_accumulator.reset()
loss = hvd.allreduce(loss)
mlm_loss = hvd.allreduce(mlm_loss)
mlm_acc = hvd.allreduce(mlm_acc)
sop_loss = hvd.allreduce(sop_loss)
sop_acc = hvd.allreduce(sop_acc)
return loss, mlm_loss, mlm_acc, sop_loss, sop_acc, grad_norm
# The bottleneck is here, since each node has 10 GiB/s PCI throughput, accumulating the gradients
# on the CPU. If there's a way to accumulate gradients on the GPU without getting OOM, let's find it!
def train_step(
model,
opt,
gradient_accumulator,
batch,
gradient_accumulation_steps: int,
skip_sop: bool,
skip_mlm: bool,
):
# MLM uses last_hidden_state, while SOP uses pooler_output
# Loss is float, acc is half
total_loss, total_mlm_loss, total_mlm_acc, total_sop_loss, total_sop_acc = (
tf.constant(0, dtype=tf.float32),
tf.constant(0, dtype=tf.float32),
tf.constant(0, dtype=tf.float32),
tf.constant(0, dtype=tf.float32),
tf.constant(0, dtype=tf.float16),
)
for step in range(gradient_accumulation_steps):
loss, mlm_loss, mlm_acc, sop_loss, sop_acc = train_batch(
model=model,
opt=opt,
gradient_accumulator=gradient_accumulator,
input_ids=batch["input_ids"][step],
attention_mask=batch["input_mask"][step],
token_type_ids=batch["segment_ids"][step],
label_positions=batch["masked_lm_positions"][step],
label_ids=batch["masked_lm_ids"][step],
label_weights=batch["masked_lm_weights"][step],
next_sentence_labels=batch["next_sentence_labels"][step],
skip_sop=skip_sop,
skip_mlm=skip_mlm,
)
total_loss += loss
total_mlm_loss += mlm_loss
total_mlm_acc += tf.cast(mlm_acc, tf.float32)
total_sop_loss += sop_loss
total_sop_acc += tf.cast(sop_acc, tf.float16)
total_loss /= gradient_accumulation_steps
total_mlm_loss /= gradient_accumulation_steps
total_mlm_acc /= gradient_accumulation_steps
total_sop_loss /= gradient_accumulation_steps
total_sop_acc /= gradient_accumulation_steps
return_tuple = allreduce(
model=model,
opt=opt,
gradient_accumulator=gradient_accumulator,
loss=total_loss,
mlm_loss=total_mlm_loss,
mlm_acc=total_mlm_acc,
sop_loss=total_sop_loss,
sop_acc=total_sop_acc,
)
return return_tuple
def validation_batch(model, batch, skip_mlm: bool, skip_sop: bool):
input_ids = batch["input_ids"]
attention_mask = batch["input_mask"]
token_type_ids = batch["segment_ids"]
label_positions = batch["masked_lm_positions"]
label_ids = batch["masked_lm_ids"]
label_weights = batch["masked_lm_weights"]
next_sentence_labels = batch["next_sentence_labels"]
input_dict = {
"input_ids": input_ids,
"attention_mask": attention_mask,
"token_type_ids": token_type_ids,
}
# mlm_logits: [b,seq_len,vocab_size]
# sop_logits: [b,2]
# hidden_states: tuple w/ len 13
mlm_logits, sop_logits, hidden_states = model(input_dict, training=False)
# MLM calculation
if skip_mlm:
mlm_loss, mlm_acc = 0, 0
else:
mlm_loss, mlm_acc = mlm_loss_fn(
prediction_logits=mlm_logits,
label_positions=label_positions,
label_ids=label_ids,
label_weights=label_weights,
)
# SOP calculation
if skip_sop:
sop_loss, sop_acc = 0, 0
else:
sop_loss, sop_acc = sop_loss_fn(
prediction_logits=sop_logits, next_sentence_labels=next_sentence_labels
)
loss = mlm_loss + sop_loss # Should there be a coefficient on one of these?
return loss, mlm_loss, mlm_acc, sop_loss, sop_acc
def run_validation(model, validation_dataset, skip_sop: bool, skip_mlm: bool):
# A single TFRecord shard contains 22663 batches, or 170k examples.
num_batches = 100
val_loss, val_mlm_loss, val_mlm_acc, val_sop_loss, val_sop_acc = (0, 0, 0, 0, 0)
for batch in validation_dataset.take(num_batches):
loss, mlm_loss, mlm_acc, sop_loss, sop_acc = validation_batch(
model=model, batch=batch, skip_sop=skip_sop, skip_mlm=skip_mlm
)
val_loss += loss
val_mlm_loss += mlm_loss
val_mlm_acc += mlm_acc
val_sop_loss += sop_loss
val_sop_acc += sop_acc
val_loss /= num_batches
val_mlm_loss /= num_batches
val_mlm_acc /= num_batches
val_sop_loss /= num_batches
val_sop_acc /= num_batches
return (val_loss, val_mlm_loss, val_mlm_acc, val_sop_loss, val_sop_acc)
def hvd_barrier():
hvd.allreduce(tf.random.normal([1]))
def get_squad_results(
model: tf.keras.Model, model_size: str, step: int, fast: bool = False, dummy_eval: bool = False,
):
# This is inefficient, since each rank will save and serialize the model separately.
# It would be better to have rank 0 save the model and all the ranks read it, but
# `run_name` isn't deterministic due to timestamps, so only rank 0 has the run_name.
# TODO: Improve. If only tf.keras.clone_model(model) worked.
with TemporaryDirectory() as dirname:
path = os.path.join(dirname, "model")
model.save_weights(path)
hvd_barrier()
cloned_model = type(model)(config=model.config)
cloned_model.load_weights(path).expect_partial()
hvd_barrier()
per_gpu_batch_size = min(3, int(math.ceil(48 / hvd.size())))
if fast:
warmup_steps = 5
total_steps = 10
dataset = "debug"
else:
warmup_steps = 814
total_steps = 8144
dataset = "squadv2"
squad_run_name = f"pretrain{step}-"
squad_results = run_squad_and_get_results(
run_name=squad_run_name,
fsx_prefix=args.fsx_prefix,
pre_layer_norm=cloned_model.config.pre_layer_norm,
model_size=model_size,
load_from=cloned_model,
load_step=None,
batch_size=per_gpu_batch_size, # This will be less than 3, so no OOM errors
checkpoint_frequency=None,
validate_frequency=None,
learning_rate=3e-5,
warmup_steps=warmup_steps,
total_steps=total_steps,
dataset=dataset,
dummy_eval=dummy_eval,
)
hvd_barrier()
if hvd.rank() == 0:
return squad_results
def wrap_global_functions(do_gradient_accumulation: bool):
global validation_batch
validation_batch = rewrap_tf_function(validation_batch)
if do_gradient_accumulation:
global train_batch
train_batch = rewrap_tf_function(train_batch)
global allreduce
allreduce = rewrap_tf_function(allreduce)
else:
global train_step
train_step = rewrap_tf_function(train_step)
def main(
fsx_prefix: str,
model_type: str,
model_size: str,
batch_size: int,
max_seq_length: int,
gradient_accumulation_steps: int,
optimizer: str,
name: str,
learning_rate: float,
end_learning_rate: float,
warmup_steps: int,
total_steps: int,
skip_sop: bool,
skip_mlm: bool,
pre_layer_norm: bool,
fast_squad: bool,
dummy_eval: bool,
squad_steps: List[int],
hidden_dropout_prob: float,
):
# Hard-coded values that don't need to be arguments
max_predictions_per_seq = 20
log_frequency = 1000
checkpoint_frequency = 5000
validate_frequency = 2000
histogram_frequency = 100
do_gradient_accumulation = gradient_accumulation_steps > 1
if hvd.rank() == 0:
# Run name should only be used on one process to avoid race conditions
current_time = datetime.datetime.now().strftime("%Y%m%d-%H%M%S")
platform = "eks" if args.fsx_prefix == "/fsx" else "sm"
if skip_sop:
loss_str = "-skipsop"
elif skip_mlm:
loss_str = "-skipmlm"
else:
loss_str = ""
amp_str = (
"-skipamp"
if not tf.config.optimizer.get_experimental_options().get("auto_mixed_precision", False)
else ""
)
ln_str = "-preln" if pre_layer_norm else "-postln"
dropout_str = f"-{hidden_dropout_prob}dropout" if hidden_dropout_prob != 0 else ""
name_str = f"-{name}" if name else ""
metadata = f"{model_type}-{model_size}-{args.load_from}-{hvd.size()}gpus-{batch_size}batch-{gradient_accumulation_steps}accum-{learning_rate}lr-{args.max_grad_norm}maxgrad-{optimizer}opt-{total_steps}steps-{max_seq_length}seq{amp_str}{ln_str}{loss_str}{dropout_str}{name_str}"
run_name = f"{current_time}-{platform}-{metadata}"
# Logging should only happen on a single process
# https://stackoverflow.com/questions/9321741/printing-to-screen-and-writing-to-a-file-at-the-same-time
level = logging.INFO
format = "%(asctime)-15s %(name)-12s: %(levelname)-8s %(message)s"
handlers = [
logging.FileHandler(f"{fsx_prefix}/logs/albert/{run_name}.log"),
logging.StreamHandler(),
]
logging.basicConfig(level=level, format=format, handlers=handlers)
# Check that arguments passed in properly, only after registering the alert_func and logging
assert not (skip_sop and skip_mlm), "Cannot use --skip_sop and --skip_mlm"
wrap_global_functions(do_gradient_accumulation)
if model_type == "albert":
model_desc = f"albert-{model_size}-v2"
elif model_type == "bert":
model_desc = f"bert-{model_size}-uncased"
config = AutoConfig.from_pretrained(model_desc)
config.pre_layer_norm = pre_layer_norm
config.output_hidden_states = True
config.hidden_dropout_prob = hidden_dropout_prob
model = TFAutoModelForPreTraining.from_config(config)
if args.load_from == "scratch":
pass
else:
assert model_type == "albert", "Only loading pretrained albert models is supported"
huggingface_name = f"albert-{model_size}-v2"
if args.load_from == "huggingface":
albert = TFAlbertModel.from_pretrained(huggingface_name, config=config)
model.albert = albert
elif args.load_from == "huggingfacepreds":
mlm_model = TFAlbertForMaskedLM.from_pretrained(huggingface_name, config=config)
model.albert = mlm_model.albert
model.cls.predictions = mlm_model.predictions
tokenizer = get_tokenizer()
schedule = LinearWarmupLinearDecaySchedule(
max_learning_rate=learning_rate,
end_learning_rate=end_learning_rate,
warmup_steps=warmup_steps,
total_steps=total_steps,
)
if optimizer == "lamb":
opt = LAMB(
learning_rate=schedule,
weight_decay_rate=0.01,
beta_1=0.9,
beta_2=0.999,
epsilon=1e-6,
exclude_from_weight_decay=["LayerNorm", "layer_norm", "bias"],
)
elif optimizer == "adam":
opt = AdamW(weight_decay=0.0, learning_rate=schedule)
opt = tf.keras.mixed_precision.experimental.LossScaleOptimizer(opt, loss_scale="dynamic")
gradient_accumulator = GradientAccumulator()
# Train filenames are [1, 2047]
# Val filenames are [0]
# Note the different subdirectories
train_glob = f"{fsx_prefix}/albert_pretraining/tfrecords/train/max_seq_len_{max_seq_length}_max_predictions_per_seq_{max_predictions_per_seq}_masked_lm_prob_15/albert_*.tfrecord"
validation_glob = f"{fsx_prefix}/albert_pretraining/tfrecords/validation/max_seq_len_{max_seq_length}_max_predictions_per_seq_{max_predictions_per_seq}_masked_lm_prob_15/albert_*.tfrecord"
train_filenames = glob.glob(train_glob)
validation_filenames = glob.glob(validation_glob)
train_dataset = get_mlm_dataset(
filenames=train_filenames,
max_seq_length=max_seq_length,
max_predictions_per_seq=max_predictions_per_seq,
batch_size=batch_size,
) # Of shape [batch_size, ...]
train_dataset = train_dataset.batch(
gradient_accumulation_steps
) # Batch of batches, helpful for gradient accumulation. Shape [grad_steps, batch_size, ...]
# train_dataset = (
# train_dataset.repeat()
# ) # One iteration with 10 dupes, 8 nodes seems to be 60-70k steps.
train_dataset = train_dataset.prefetch(buffer_size=8)
# Validation should only be done on one node, since Horovod doesn't allow allreduce on a subset of ranks
if hvd.rank() == 0:
validation_dataset = get_mlm_dataset(
filenames=validation_filenames,
max_seq_length=max_seq_length,
max_predictions_per_seq=max_predictions_per_seq,
batch_size=batch_size,
)
# validation_dataset = validation_dataset.batch(1)
validation_dataset = validation_dataset.prefetch(buffer_size=8)
pbar = tqdm.tqdm(total_steps)
summary_writer = None # Only create a writer if we make it through a successful step
if hvd.rank() == 0:
logger.info(f"Starting training, job name {run_name}")
for i, batch in enumerate(train_dataset):
learning_rate = schedule(step=tf.constant(i, dtype=tf.float32))
loss, mlm_loss, mlm_acc, sop_loss, sop_acc, grad_norm = train_step(
model=model,
opt=opt,
gradient_accumulator=gradient_accumulator,
batch=batch,
gradient_accumulation_steps=gradient_accumulation_steps,
skip_sop=skip_sop,
skip_mlm=skip_mlm,
)
# Don't want to wrap broadcast_variables() in a tf.function, can lead to asynchronous errors
if i == 0:
hvd.broadcast_variables(model.variables, root_rank=0)
hvd.broadcast_variables(opt.variables(), root_rank=0)
is_final_step = i >= total_steps - 1
do_squad = i in squad_steps or is_final_step
# Squad requires all the ranks to train, but results are only returned on rank 0
if do_squad:
squad_results = get_squad_results(
model=model, model_size=model_size, step=i, fast=fast_squad, dummy_eval=dummy_eval,
)
if hvd.rank() == 0:
squad_exact, squad_f1 = squad_results["exact"], squad_results["f1"]
logger.info(f"SQuAD step {i} -- F1: {squad_f1:.3f}, Exact: {squad_exact:.3f}")
# Re-wrap autograph so it doesn't get arg mismatches
wrap_global_functions(do_gradient_accumulation)
if hvd.rank() == 0:
do_log = i % log_frequency == 0
do_checkpoint = (i % checkpoint_frequency == 0) or is_final_step
do_validation = (i % validate_frequency == 0) or is_final_step
pbar.update(1)
description = f"Loss: {loss:.3f}, MLM: {mlm_loss:.3f}, SOP: {sop_loss:.3f}, MLM_acc: {mlm_acc:.3f}, SOP_acc: {sop_acc:.3f}"
pbar.set_description(description)
if do_log:
logger.info(f"Train step {i} -- {description}")
if do_checkpoint:
checkpoint_path = f"{fsx_prefix}/checkpoints/albert/{run_name}-step{i}.ckpt"
logger.info(f"Saving checkpoint at {checkpoint_path}")
model.save_weights(checkpoint_path)
# model.load_weights(checkpoint_path)
if do_validation:
val_loss, val_mlm_loss, val_mlm_acc, val_sop_loss, val_sop_acc = run_validation(
model=model,
validation_dataset=validation_dataset,
skip_sop=skip_sop,
skip_mlm=skip_mlm,
)
description = f"Loss: {val_loss:.3f}, MLM: {val_mlm_loss:.3f}, SOP: {val_sop_loss:.3f}, MLM_acc: {val_mlm_acc:.3f}, SOP_acc: {val_sop_acc:.3f}"
logger.info(f"Validation step {i} -- {description}")
# Create summary_writer after the first step
if summary_writer is None:
summary_writer = tf.summary.create_file_writer(
f"{fsx_prefix}/logs/albert/{run_name}"
)
# Log to TensorBoard
weight_norm = tf.math.sqrt(
tf.math.reduce_sum([tf.norm(var, ord=2) ** 2 for var in model.trainable_variables])
)
with summary_writer.as_default():
tf.summary.scalar("weight_norm", weight_norm, step=i)
tf.summary.scalar("learning_rate", learning_rate, step=i)
tf.summary.scalar("train_loss", loss, step=i)
tf.summary.scalar("train_mlm_loss", mlm_loss, step=i)
tf.summary.scalar("train_mlm_acc", mlm_acc, step=i)
tf.summary.scalar("train_sop_loss", sop_loss, step=i)
tf.summary.scalar("train_sop_acc", sop_acc, step=i)
tf.summary.scalar("grad_norm", grad_norm, step=i)
if do_validation:
tf.summary.scalar("val_loss", val_loss, step=i)
tf.summary.scalar("val_mlm_loss", val_mlm_loss, step=i)
tf.summary.scalar("val_mlm_acc", val_mlm_acc, step=i)
tf.summary.scalar("val_sop_loss", val_sop_loss, step=i)
tf.summary.scalar("val_sop_acc", val_sop_acc, step=i)
if do_squad:
tf.summary.scalar("squad_f1", squad_f1, step=i)
tf.summary.scalar("squad_exact", squad_exact, step=i)
if is_final_step:
break
if hvd.rank() == 0:
pbar.close()
logger.info(f"Finished pretraining, job name {run_name}")
def get_squad_steps(extra_steps_str: str) -> List[int]:
""" Parse a comma-separated string of integers, append it to list of default steps. """
extra_squad_steps = [int(val) for val in extra_steps_str.split(",")] if extra_steps_str else []
default_squad_steps = [k * 1000 for k in [5, 10, 20, 40, 60, 80, 100, 120]]
return extra_squad_steps + default_squad_steps
if __name__ == "__main__":
parser = argparse.ArgumentParser()
parser.add_argument("--model_dir", help="Unused, but passed by SageMaker")
parser.add_argument("--model_type", default="albert", choices=["albert", "bert"])
parser.add_argument("--model_size", default="base", choices=["base", "large"])
parser.add_argument("--batch_size", type=int, default=32, help="per GPU")
parser.add_argument("--gradient_accumulation_steps", type=int, default=2)
parser.add_argument("--max_seq_length", type=int, default=512, choices=[128, 512])
parser.add_argument("--warmup_steps", type=int, default=3125)
parser.add_argument("--total_steps", type=int, default=125000)
parser.add_argument("--learning_rate", type=float, default=0.00176)
parser.add_argument("--end_learning_rate", type=float, default=3e-5)
parser.add_argument("--hidden_dropout_prob", type=float, default=0.0)
parser.add_argument("--max_grad_norm", type=float, default=1.0)
parser.add_argument("--optimizer", default="lamb", choices=["lamb", "adam"])
parser.add_argument("--name", default="", help="Additional info to append to metadata")
parser.add_argument(
"--load_from", default="scratch", choices=["scratch", "huggingface", "huggingfacepreds"],
)
parser.add_argument(
"--fsx_prefix",
default="/fsx",
choices=["/fsx", "/opt/ml/input/data/training"],
help="Change to /opt/ml/input/data/training on SageMaker",
)
# SageMaker does not work with 'store_const' args, since it parses into a dictionary
# We will treat any value not equal to None as True, and use --skip_amp=True
parser.add_argument(
"--skip_amp",
choices=["true"],
help="For debugging. Faster startup time, slower runtime, more GPU vRAM.",
)
parser.add_argument(
"--skip_xla",
choices=["true"],
help="For debugging. Faster startup time, slower runtime, more GPU vRAM.",
)
parser.add_argument(
"--eager",
choices=["true"],
help="For debugging. Faster launch, slower runtime, more GPU vRAM.",
)
parser.add_argument(
"--skip_sop", choices=["true"], help="Only use MLM loss, and exclude the SOP loss.",
)
parser.add_argument(
"--skip_mlm", choices=["true"], help="Only use SOP loss, and exclude the MLM loss.",
)
parser.add_argument(
"--pre_layer_norm",
choices=["true"],
help="Place layer normalization before the attention & FFN, rather than after adding the residual connection. https://openreview.net/pdf?id=B1x8anVFPr",
)
parser.add_argument("--extra_squad_steps", type=str)
parser.add_argument("--fast_squad", choices=["true"])
parser.add_argument("--dummy_eval", choices=["true"])
args = parser.parse_args()
tf.random.set_seed(42)
tf.autograph.set_verbosity(0)
# Horovod init
hvd.init()
gpus = tf.config.list_physical_devices("GPU")
for gpu in gpus:
tf.config.experimental.set_memory_growth(gpu, True)
if gpus:
tf.config.set_visible_devices(gpus[hvd.local_rank()], "GPU")
# XLA, AMP, AutoGraph
parse_bool = lambda arg: arg == "true"
tf.config.optimizer.set_jit(not parse_bool(args.skip_xla))
tf.config.optimizer.set_experimental_options(
{"auto_mixed_precision": not parse_bool(args.skip_amp)}
)
tf.config.experimental_run_functions_eagerly(parse_bool(args.eager))
main(
fsx_prefix=args.fsx_prefix,
model_type=args.model_type,
model_size=args.model_size,
batch_size=args.batch_size,
max_seq_length=args.max_seq_length,
gradient_accumulation_steps=args.gradient_accumulation_steps,
optimizer=args.optimizer,
name=args.name,
learning_rate=args.learning_rate,
end_learning_rate=args.end_learning_rate,
warmup_steps=args.warmup_steps,
total_steps=args.total_steps,
skip_sop=parse_bool(args.skip_sop),
skip_mlm=parse_bool(args.skip_mlm),
pre_layer_norm=parse_bool(args.pre_layer_norm),
fast_squad=parse_bool(args.fast_squad),
dummy_eval=parse_bool(args.dummy_eval),
squad_steps=get_squad_steps(args.extra_squad_steps),
hidden_dropout_prob=args.hidden_dropout_prob,
)
|
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] |
src/backend/qaweb/connectors.py
|
MovElb/Ann
| 0 |
134810
|
import os
import sys
import urllib.parse
import aiohttp
import aioredis
import aiowiki
import ujson
from typing import Any, List, Dict
from aiohttp import web
from aioredis import Redis
class BaseConnector:
def __init__(self, url: str, read_timeout: int, conn_timeout: int):
self._url = url
self._read_timeout = read_timeout
self._conn_timeout = conn_timeout
self._connector = aiohttp.TCPConnector(
use_dns_cache=True, ttl_dns_cache=60 * 60, limit=1024
)
self._sess = aiohttp.ClientSession(
connector=self._connector,
read_timeout=self._read_timeout,
conn_timeout=self._conn_timeout,
json_serialize=ujson.dumps,
)
class SaaSConnector(BaseConnector):
def __init__(self, *args: Any, **kwargs: Any):
super().__init__(*args, **kwargs)
self._url = self._url.format(os.environ['GAPI_KEY'], os.environ['GAPI_CX'])
self._wiki = aiowiki.Wiki.wikipedia("en")
async def get_documents(self, query, limit: int = 10) -> List[str]:
quote_encoded = urllib.parse.quote(query)
async with self._sess.get(self._url + quote_encoded) as resp:
resp.raise_for_status()
google_serp: Dict = await resp.json(loads=ujson.loads)
urls = [item['link'] for item in google_serp.get('items', list())][:limit]
titles = list(map(lambda u: u.split('/')[-1], urls))
return titles
def get_wikipage(self, title) -> aiowiki.Page:
return self._wiki.get_page(title)
async def process_query(self, query) -> str:
pass
class RedisConnector:
def __init__(self, host: str, port: int, master_name: str):
self._host = host
self._port = port
self._master_name = master_name
self._SUF_TEXT = '--TEXT'
self._SUF_PREPRO = '--PREPRO'
async def connect(self):
print((self._host, self._port), self._master_name, flush=True, file=sys.stdout)
sentinel = await aioredis.create_sentinel([(self._host, self._port)])
self._master: Redis = sentinel.master_for(self._master_name)
async def get_text(self, title) -> str:
plain = await self._master.get(title + self._SUF_TEXT)
if plain:
return plain.decode('utf-8')
return None
async def get_preprocessed(self, title):
plain = await self._master.get(title + self._SUF_PREPRO)
if plain:
ujson.loads(plain.decode('utf-8'))
return None
async def dump_text(self, title, text):
await self._master.set(title + self._SUF_TEXT, text)
async def dump_prepro_text(self, title, prepro_text):
await self._master.set(title + self._SUF_PREPRO, ujson.dumps(prepro_text))
class NetConnector(BaseConnector):
def __init__(self, *args: Any, **kwargs: Any):
super().__init__(*args, **kwargs)
async def get_answer(self, preprocessed) -> Dict:
if len(preprocessed) == 0:
return {}
async with self._sess.post(self._url, json={'data': preprocessed}) as resp:
resp.raise_for_status()
return await resp.json()
def setup_connectors(app: web.Application) -> None:
app['saas'] = SaaSConnector(**app['config']['saas'])
app['net'] = NetConnector(**app['config']['net'])
app['redis'] = RedisConnector(**app['config']['redis'])
|
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1839,
2917,
16215,
1127,
275,
11287,
13,
2
] |
python/461.hamming-distance.py
|
stavanmehta/leetcode
| 0 |
1602548
|
class Solution:
def hammingDistance(self, x: int, y: int) -> int:
|
[
1,
770,
24380,
29901,
13,
1678,
822,
16366,
4056,
27469,
29898,
1311,
29892,
921,
29901,
938,
29892,
343,
29901,
938,
29897,
1599,
938,
29901,
13,
308,
13,
2
] |
wefe/word_embedding_model.py
|
raffaem/wefe
| 0 |
131715
|
<filename>wefe/word_embedding_model.py
from gensim.models.keyedvectors import BaseKeyedVectors
class WordEmbeddingModel:
"""A container for Word Embedding pre-trained models.
It can hold gensim's KeyedVectors or gensim's api loaded models.
It includes the name of the model and some vocab prefix if needed.
"""
def __init__(self,
word_embedding: BaseKeyedVectors,
model_name: str = None,
vocab_prefix: str = None):
"""Initializes the WordEmbeddingModel container.
Parameters
----------
keyed_vectors : BaseKeyedVectors.
An instance of word embedding loaded through gensim KeyedVector
interface or gensim's api.
model_name : str, optional
The name of the model, by default ''.
vocab_prefix : str, optional.
A prefix that will be concatenated with all word in the model
vocab, by default None.
Raises
------
TypeError
if word_embedding is not a KeyedVectors instance.
TypeError
if model_name is not None and not instance of str.
TypeError
if vocab_prefix is not None and not instance of str.
Examples
--------
>>> from gensim.test.utils import common_texts
>>> from gensim.models import Word2Vec
>>> from wefe.word_embedding_model import WordEmbeddingModel
>>> dummy_model = Word2Vec(common_texts, size=10, window=5,
... min_count=1, workers=1).wv
>>> model = WordEmbeddingModel(dummy_model, 'Dummy model dim=10',
... vocab_prefix='/en/')
>>> print(model.model_name_)
Dummy model dim=10
>>> print(model.vocab_prefix_)
/en/
Attributes
----------
model_ : KeyedVectors
The object that contains the model.
model_name_ : str
The name of the model.
vocab_prefix_ : str
A prefix that will be concatenated with each word of the vocab
of the model.
"""
if not isinstance(word_embedding, BaseKeyedVectors):
raise TypeError('word_embedding must be an instance of a gensim\'s'
' KeyedVectors. Given: {}'.format(word_embedding))
else:
self.model_ = word_embedding
if model_name is None:
self.model_name_ = 'Unnamed word embedding model'
elif not isinstance(model_name, str):
raise TypeError(
'model_name must be a string. Given: {}'.format(model_name))
else:
self.model_name_ = model_name
if vocab_prefix is None:
self.vocab_prefix_ = ''
elif not isinstance(vocab_prefix, str):
raise TypeError(
'vocab_prefix parameter must be a string. Given: {}'.format(
vocab_prefix))
else:
self.vocab_prefix_ = vocab_prefix
def __eq__(self, other):
if self.model_ != other.model_:
return False
if self.model_name_ != other.model_name_:
return False
return True
|
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] |
MISSIONS/bvr_sim/agent/dummy_observer/attack_seq_adjust.py
|
binary-husky/hmp2g
| 2 |
142380
|
<filename>MISSIONS/bvr_sim/agent/dummy_observer/attack_seq_adjust.py
from typing import List
from ..agent import Agent
from ..env_cmd import CmdEnv
from UTILS.colorful import *
from UTILS.tensor_ops import dir2rad, np_softmax, reg_rad_at, reg_rad, repeat_at
from .maneuver import maneuver_angle_to_ms, maneuver_angle_to_ms3d, maneuver_speed_to_ms, maneuver_vip
import copy
import random
import numpy as np
import time
from .tools import distance_matrix
from .base import Baseclass, Special, Drone, Vip, Plane, MS
from .missile_policy import MS_policy
from .emergent import Emergent
Init_attack_order_adjust_dis = 150e3
class Attack_Adjust():
# 只执行一次的初始化调整，敌我距离小于 Init_attack_order_adjust_dis 时执行一次
def init_adjust(self):
op_list = np.array([op for op in self.op_planes], dtype=object)
op_dis_2_vip = [self.get_dis(
op.ID, p.ID) for op in self.op_planes
for p in self.my_planes if p.is_vip]
assert len(op_dis_2_vip) == len(self.op_planes)
sorted_index = np.argsort(op_dis_2_vip)
op_list_sorted = op_list[sorted_index]
op_attk_by_squad_1 = op_list_sorted[0::2]
op_attk_by_squad_2 = op_list_sorted[1::2]
op_list1_has_op_vip = any([op.is_vip for op in op_attk_by_squad_1])
op_list2_has_op_vip = any([op.is_vip for op in op_attk_by_squad_2])
op_vip = [op for op in self.op_planes if op.is_vip][0]
assert (op_list1_has_op_vip and (not op_list2_has_op_vip)) or (
op_list2_has_op_vip and (not op_list1_has_op_vip))
if op_list2_has_op_vip:
t = op_attk_by_squad_1
op_attk_by_squad_1 = op_attk_by_squad_2
op_attk_by_squad_2 = t
squad_1_mem = [p for p in self.my_planes if p.squad_name == "U1"]
squad_2_mem = [p for p in self.my_planes if p.squad_name == "U2"]
for p in squad_1_mem:
p.attack_order = [op.Name for op in op_attk_by_squad_1]
# 如果攻击序列中没有有人机，则把有人机放到最后
if not any(['有人机' in p_name for p_name in p.attack_order]):
p.attack_order.append(op_vip.Name)
# ## print亮绿(p.Name, '分配战机进攻序列：', p.attack_order)
for p in squad_2_mem:
p.attack_order = [op.Name for op in op_attk_by_squad_2]
# 如果攻击序列中没有有人机，则把有人机放到最后
if not any(['有人机' in p_name for p_name in p.attack_order]):
p.attack_order.append(op_vip.Name)
# ## print亮蓝(p.Name, '分配战机进攻序列：', p.attack_order)
pass
def new_init_adjust(self):
all_my_plane_center = np.array([p.pos2d for p in self.my_planes])
all_my_plane_center = all_my_plane_center.mean(axis=0)
# 找出敌方处于边缘的两架飞机
angle = -9999
op1_tmp = None
op2_tmp = None
for op1 in self.op_planes:
for op2 in self.op_planes:
if op1 is op2: continue
delta_deg = self.get_points_angle_deg(pt1=op1.pos2d,
pt_center=all_my_plane_center,
pt2=op2.pos2d)
if delta_deg > angle:
angle = delta_deg
op1_tmp = op1
op2_tmp = op2
# 以其中的op1作为角度零点，再进行计算排序
new_op_rank = [self.get_points_angle_deg(
op1_tmp.pos2d,
all_my_plane_center,
op.pos2d) for op in self.op_planes]
sorted_index = np.argsort(new_op_rank)
op_list = np.array([op for op in self.op_planes], dtype=object)
op_list_sorted = op_list[sorted_index]
op_attk_by_squad_1 = op_list_sorted
op_attk_by_squad_2 = list(reversed(op_list_sorted))
squad_1_mem = [p for p in self.my_planes if p.squad_name == "U1"]
squad_2_mem = [p for p in self.my_planes if p.squad_name == "U2"]
for p in squad_1_mem:
p.attack_order = [op.Name for op in op_attk_by_squad_1]
for p in squad_2_mem:
p.attack_order = [op.Name for op in op_attk_by_squad_2]
# op_attk_by_squad_2 = op_list_sorted[1::2]
# op_list1_has_op_vip = any([op.is_vip for op in op_attk_by_squad_1])
# op_list2_has_op_vip = any([op.is_vip for op in op_attk_by_squad_2])
# op_vip = [op for op in self.op_planes if op.is_vip][0]
# assert (op_list1_has_op_vip and (not op_list2_has_op_vip)) or (
# op_list2_has_op_vip and (not op_list1_has_op_vip))
# if op_list2_has_op_vip:
# t = op_attk_by_squad_1
# op_attk_by_squad_1 = op_attk_by_squad_2
# op_attk_by_squad_2 = t
# # 如果攻击序列中没有有人机，则把有人机放到最后
# if not any(['有人机' in p_name for p_name in p.attack_order]):
# p.attack_order.append(op_vip.Name)
# # ## print亮绿(p.Name, '分配战机进攻序列：', p.attack_order)
# # 如果攻击序列中没有有人机，则把有人机放到最后
# if not any(['有人机' in p_name for p_name in p.attack_order]):
# p.attack_order.append(op_vip.Name)
# pass
# 根据敌方每个飞机的剩余的弹药量，变动进攻序列，将无弹的目标放到最末的优先级
# 调整的条件：1、敌方无弹，2、没有正在往这个敌方目标飞行的导弹
def adjust_by_left_ammo(self):
for p in self.my_planes:
attack_list = [self.find_plane_by_name(op_name) for op_name in p.attack_order]
attack_list = [op for op in attack_list if op is not None]
new_attack_list_normal = []
new_attack_list_mvback = []
for i in range(len(attack_list)):
missile_towards_op = [ms for ms in self.ms if ms.EngageTargetID==attack_list[i].ID and ms.ms_at_terminal]
require_no_ms_guidence = (len(missile_towards_op)==0)
if attack_list[i].OpLeftWeapon == 0 and attack_list[i].is_drone and require_no_ms_guidence:
new_attack_list_mvback.append(attack_list[i])
else:
new_attack_list_normal.append(attack_list[i])
new_attack_list = new_attack_list_normal + new_attack_list_mvback
p.attack_order = [op.Name for op in new_attack_list]
pass
# 调整每个飞机的进攻序列
def adjust_attack_order(self):
'''
# 在敌我距离缩小到阈值时，第一次调整进攻序列
min_distance = min([self.get_dis(op.ID, p.ID) for op in self.op_planes for p in self.my_planes])
if min_distance < Init_attack_order_adjust_dis:
if not self.initial_attack_order_adjusted:
self.initial_attack_order_adjusted = True
self.init_adjust()
'''
min_distance = min([self.get_dis(op.ID, p.ID) for op in self.op_planes for p in self.my_planes])
if min_distance > Init_attack_order_adjust_dis:
self.new_init_adjust()
# 根据将敌方的无弹目标放到末优先级
self.adjust_by_left_ammo()
'''
# 当敌方的有人机距离小队中全部成员的距离小于50， 或者距离任意成员的距离小于30，小队内全体成员前往歼灭敌方有人机
op_vip = self.find_plane_by_name(self.op_color + "有人机")
if op_vip is None: return
def attack_vip_first(p):
# ## print亮紫(p.attack_order)
p.attack_order = [op_name for op_name in p.attack_order if "有人机" in op_name] + \
[op_name for op_name in p.attack_order if "有人机" not in op_name]
# ## print亮蓝(p.attack_order)
pass
squads = ['U1', 'U2']
for squad in squads:
p_list = self.find_planes_by_squad(squad)
if len(p_list)==0: continue
dis2_op_vip = [self.get_dis(p.ID, op_vip.ID) for p in p_list]
if min(dis2_op_vip) < 30e3:
# ## print亮紫('小队全体攻击有人机')
for p in p_list: attack_vip_first(p)
elif max(dis2_op_vip) < 50e3:
# ## print亮紫('小队全体攻击有人机')
for p in p_list: attack_vip_first(p)
'''
return
def adjust_attack_order_phase2(self):
for p in self.my_planes:
if hasattr(p, 'ms_policy_suggested_target') and p.ms_policy_suggested_target is not None:
# 根据 ms_policy_suggested_target 调整攻击序列
attack_list = [self.find_plane_by_name(op_name) for op_name in p.attack_order]
attack_list = [op for op in attack_list if op is not None]
new_attack_list_normal = []
new_attack_list_mvback = []
for i in range(len(attack_list)):
if attack_list[i].ID == p.ms_policy_suggested_target.ID:
new_attack_list_mvback.append(attack_list[i])
else:
new_attack_list_normal.append(attack_list[i])
new_attack_list = [p.ms_policy_suggested_target] + new_attack_list_normal + new_attack_list_mvback
p.attack_order = [op.Name for op in new_attack_list]
|
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] |
training_with_dt.py
|
AlfredYang1986/phcleanning
| 0 |
1606261
|
<gh_stars>0
# -*- coding: utf-8 -*-
"""<EMAIL>.
功能描述：job3：left join cpa和prod
* @author yzy
* @version 0.0
* @since 2020/08/12
* @note 落盘数据：cpa_prod_join
"""
import os
from pyspark.sql import SparkSession
from dataparepare import *
from interfere import *
from pyspark.sql.types import *
from pyspark.sql.functions import desc
from pyspark.sql.functions import rank
from pyspark.sql import Window
from pyspark.ml.linalg import Vectors, VectorUDT
from pyspark.ml.classification import MultilayerPerceptronClassifier
from pyspark.ml import Pipeline
from pyspark.ml.classification import DecisionTreeClassifier
from pyspark.ml.feature import StringIndexer, VectorIndexer
from pyspark.ml.evaluation import MulticlassClassificationEvaluator
def prepare():
os.environ["PYSPARK_PYTHON"] = "python3"
# 读取s3桶中的数据
spark = SparkSession.builder \
.master("yarn") \
.appName("CPA&GYC match refactor") \
.config("spark.driver.memory", "1g") \
.config("spark.executor.cores", "1") \
.config("spark.executor.instances", "2") \
.config("spark.executor.memory", "2g") \
.config('spark.sql.codegen.wholeStage', False) \
.getOrCreate()
access_key = os.getenv("AWS_ACCESS_KEY_ID")
secret_key = os.getenv("AWS_SECRET_ACCESS_KEY")
if access_key is not None:
spark._jsc.hadoopConfiguration().set("fs.s3a.access.key", access_key)
spark._jsc.hadoopConfiguration().set("fs.s3a.secret.key", secret_key)
spark._jsc.hadoopConfiguration().set("fs.s3a.impl","org.apache.hadoop.fs.s3a.S3AFileSystem")
spark._jsc.hadoopConfiguration().set("com.amazonaws.services.s3.enableV4", "true")
# spark._jsc.hadoopConfiguration().set("fs.s3a.aws.credentials.provider","org.apache.hadoop.fs.s3a.BasicAWSCredentialsProvider")
spark._jsc.hadoopConfiguration().set("fs.s3a.endpoint", "s3.cn-northwest-1.amazonaws.com.cn")
return spark
if __name__ == '__main__':
spark = prepare()
# 0. load the cleanning data
df_cleanning = load_training_data(spark).select("id").distinct()
# Split the data into training and test sets (30% held out for testing)
(df_training, df_test) = df_cleanning.randomSplit([0.7, 0.3])
# 1. load the training data
# 准备训练集合
df_result = load_training_data(spark)
df_result = df_result.select("id", "label", "features")
labelIndexer = StringIndexer(inputCol="label", outputCol="indexedLabel").fit(df_result)
featureIndexer = VectorIndexer(inputCol="features", outputCol="indexedFeatures", maxCategories=6).fit(df_result)
df_training.show(10)
# 1.1 构建训练集合
df_training = df_training.join(df_result, how="left", on="id")
df_training.show()
# 1.2 构建测试集合
df_test = df_test.join(df_result, how="left", on="id")
df_test.show()
# Train a DecisionTree model.
dt = DecisionTreeClassifier(labelCol="indexedLabel", featuresCol="indexedFeatures")
# Chain indexers and tree in a Pipeline
pipeline = Pipeline(stages=[labelIndexer, featureIndexer, dt])
# Train model. This also runs the indexers.
model = pipeline.fit(df_training)
# Make predictions.
df_predictions = model.transform(df_test)
# Select example rows to display.
df_predictions.show(10)
df_predictions.select("prediction", "indexedLabel", "features").show(5)
# Select (prediction, true label) and compute test error
evaluator = MulticlassClassificationEvaluator(
labelCol="indexedLabel", predictionCol="prediction", metricName="accuracy")
accuracy = evaluator.evaluate(df_predictions)
print("Test Error = %g " % (1.0 - accuracy))
treeModel = model.stages[2]
# summary only
print(treeModel)
# model.write().overwrite().save("s3a://ph-max-auto/2020-08-11/BPBatchDAG/refactor/alfred/dt")
model.write().overwrite().save("s3a://ph-max-auto/2020-08-11/BPBatchDAG/refactor/zyyin/pfizer_model/0.0.4/model")
|
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] |
src/poretitioner/utils/filtering.py
|
uwmisl/poretitioner
| 2 |
7180
|
"""
=========
filtering.py
=========
This module provides more granular filtering for captures.
You can customize your own filters too.
"""
from __future__ import annotations
import re
from abc import ABC, ABCMeta, abstractmethod
from dataclasses import dataclass
from json import JSONEncoder
from pathlib import PosixPath
from typing import (
Any,
Dict,
Iterable,
Mapping,
NewType,
Optional,
Protocol,
Type,
TypedDict,
Union,
)
import h5py
import numpy as np
from h5py import File as Fast5File
from ..hdf5 import (
HasFast5,
HDF5_Group,
HDF5_GroupSerialableDataclass,
HDF5_GroupSerializable,
HDF5_GroupSerializing,
IsAttr,
)
from ..logger import Logger, getLogger
from ..signals import Capture
from .core import NumpyArrayLike, PathLikeOrString, ReadId, stripped_by_keys
from .plugin import Plugin
CaptureOrTimeSeries = Union[Capture, NumpyArrayLike]
# Unique identifier for a collection of filters (e.g. "ProfJeffsAwesomeFilters")
FilterSetId = NewType("FilterSetId", str)
# Unique identifier for an individual filter (e.g. "min_frac")
FilterName = NewType("FilterName", str)
__all__ = [
"does_pass_filters",
"get_filters",
"FilterName",
"FilterSetId",
"FilterConfig",
"Filter",
"Filters",
"DEFAULT_FILTER_PLUGINS",
"FilterSet",
"FilterConfigs",
"FilterPlugin",
"PATH",
]
@dataclass(frozen=True)
class FILTER_PATH:
ROOT = f"/Filter/"
@classmethod
def filter_set_path(cls, filter_set_id: FilterSetId) -> str:
filter_path = str(PosixPath(FILTER_PATH.ROOT, filter_set_id))
return filter_path
@classmethod
def filter_set_pass_path(cls, filter_set_id: FilterSetId) -> str:
pass_path = str(PosixPath(FILTER_PATH.filter_set_path(filter_set_id), "pass"))
return pass_path
@classmethod
def filter_set_pass_path_for_read_id(cls, filter_set_id: FilterSetId, read_id: ReadId) -> str:
pass_path = str(PosixPath(FILTER_PATH.filter_set_pass_path(filter_set_id), read_id))
return pass_path
class FilterConfig(TypedDict):
"""A blueprint for how to construct a FilterPlugin.
Contains a name, and any number of other attributes
Note on terminology:
- FilterConfig: A high-level description of a filter.
- FilterPlugin: An actual, callable, implementation of a FilterConfig.
For custom plugins, make sure "filepath" is an attribute that points to the file to laod
"""
# Mapping of a FilterName to filter configurations.
FilterConfigs = NewType("FilterConfigs", Dict[FilterName, FilterConfig])
# TODO: Filter Plugin should check that name is unique. https://github.com/uwmisl/poretitioner/issues/91
class FilterPlugin(Plugin):
"""
Abstract class for Filter plugins. To write your own filter, subclass this abstract
class and implement the `apply` method and `name` property.
"""
@classmethod
@abstractmethod
def name(cls) -> str:
"""Unique name for this filter.
Make sure it doesn't conflict with any existing names.
Returns
-------
str
The unique name for this filter (e.g. "fourier_transform").
Raises
------
NotImplementedError
Raised if this filter is called without this name method being implemented.
"""
raise NotImplementedError(
"'name' class method not implemented for filter. This class method should return a unique name for this filter."
)
@abstractmethod
def apply(self, capture: CaptureOrTimeSeries) -> bool:
"""Returns True if a capture passes a given filter criteria.
For instance, a range filter would check that a capture's summary statistsics lie within a given range.
Parameters
----------
capture : np.typing.ArrayLike
Time series capture to filter.
Returns
-------
bool
Whether this capture passes the filter.
Raises
------
NotImplementedError
Raised when the filter method isn't implemented by the consuming Filter class
"""
raise NotImplementedError(
"'apply' method not implemented for filter. This method should return True if and only if applied to a capture that meets the filter criterion. For instance, "
)
def __call__(self, capture: CaptureOrTimeSeries) -> bool:
"""Apply the filter.
Defining `__call__` lets us do nice things like:
class MyCustomFilter(FilterPlugin):
def apply(capture):
# ...
pass
# Later in code where filtering is done....
valid_captures = []
filters = [ MyCustomFilter(), AnotherCustomFilter(), ... ]
valid_captures = [capture for capture in captures if all([filt(capture) for filt in filters])]
for capture in captures: # You'd want to parallelize this in a real life example...
for filt in filters:
filtered_captures = filt(capture).
Parameters
----------
capture : CaptureOrTimeSeries
Capture to filter.
Returns
-------
bool
Whether this capture passes the filter.
"""
result = self.apply(capture)
return result
RANGE_FILTER_DEFAULT_MINIMUM: float = -np.inf
RANGE_FILTER_DEFAULT_MAXIMUM: float = np.inf
class RangeFilter(FilterPlugin):
def __init__(self, minimum: Optional[float] = None, maximum: Optional[float] = None):
"""A filter that filters based on whether a signal falls between a maximum and a minimum.
Parameters
----------
minimum : float, optional
The smallest value this signal should be allowed to take (inclusive), by default RangeFilter.DEFAULT_MINIMUM
maximum : float, optional
The largest value this signal should be allowed to take (inclusive), by default RangeFilter.DEFAULT_MAXIMUM
"""
self.minimum = minimum if minimum is not None else RANGE_FILTER_DEFAULT_MINIMUM
self.maximum = maximum if maximum is not None else RANGE_FILTER_DEFAULT_MAXIMUM
def extract(self, capture: CaptureOrTimeSeries) -> NumpyArrayLike:
"""Extracts a summary statistic from the capture (e.g. mean, length, standard deviation).
Identity operation by default (just returns the capture).
You can use this function to transform the data in a useful way before processing it (e.g.
getting the mean value of a capture before filtering based on that mean.)
Note: If we picture the filtering workflow as an ETL (Extract-Transform-Load) pipeline, this would be the "transform"
(take data, modify it for a later purpose), but I feel that "transform" is perhaps a misleading function name in this context.
Parameters
----------
capture : CaptureOrTimeSeries
Capture from which to extract data.
"""
try:
signal = capture.fractionalized()
except AttributeError:
signal = capture
else:
signal = capture
return signal
# signal = getattr(capture, Capture.fractionalized.__name__, capture)
def is_in_range(self, value: Union[NumpyArrayLike, float]) -> bool:
try:
# If the value is just a float, we can use this handy syntax:
return self.minimum <= value <= self.maximum
except ValueError:
# But we're not allowed to use that syntax on numpy arrays.
return all(np.logical_and(self.minimum <= value, value <= self.maximum))
def apply(self, signal):
value = self.extract(signal)
return self.is_in_range(value)
class StandardDeviationFilter(RangeFilter):
"""Filters for captures with standard deviations in some range."""
@classmethod
def name(cls) -> str:
return "stdv"
def extract(self, capture: CaptureOrTimeSeries):
signal = super().extract(capture)
return np.std(signal)
class MeanFilter(RangeFilter):
"""Filters for captures with an arithmetic mean within a range."""
@classmethod
def name(cls) -> str:
return "mean"
def extract(self, capture: CaptureOrTimeSeries):
signal = super().extract(capture)
return np.mean(signal)
class MedianFilter(RangeFilter):
"""Filters for captures with a median within a range."""
@classmethod
def name(cls) -> str:
return "median"
def extract(self, capture: CaptureOrTimeSeries):
signal = super().extract(capture)
return np.median(signal)
class MinimumFilter(RangeFilter):
"""Filters for captures with a minimum within a range."""
@classmethod
def name(cls) -> str:
return "min"
def extract(self, capture: CaptureOrTimeSeries):
signal = super().extract(capture)
return np.min(signal)
class MaximumFilter(RangeFilter):
"""Filters for captures with a maximum within a range."""
@classmethod
def name(cls) -> str:
return "max"
def extract(self, capture: CaptureOrTimeSeries):
signal = super().extract(capture)
return np.max(signal)
class LengthFilter(RangeFilter):
"""Filters captures based on their length."""
@classmethod
def name(cls) -> str:
return "length"
def extract(self, capture: CaptureOrTimeSeries):
signal = super().extract(capture)
return len(signal)
class EjectedFilter(FilterPlugin):
"""Filters captures based on whether they were ejected from the pore."""
@classmethod
def name(cls) -> str:
return "ejected"
def extract(self, capture: Capture):
return capture.ejected
"""
How to Create Your Own Custom Filter:
Need more advanced filtering than what we provide out of the box? No problem.
Create your own custom filter by inheriting from the FilterPlugin class.
For this example, let's do something complex. Say you only want to examine captures
that have more than 5 samples with a hyperbolic tangent greater than some threshold.
That means our custom filter's `apply` function should return True if and only if
the signal has more than 5 samples greater than the threshold, after taking the hyperbolic tangent in `extract`.
"""
class MyCustomFilter(FilterPlugin):
threshold: float = 0.5 # Totally arbitrary.
def name(self):
return "foo"
def extract(self, capture):
# Do the transformations here, or pre-process it before the filter.
# Gets the hyperbolic tangent of the signal.
extracted = np.tanh(capture.signal)
return extracted
def apply(self, signal):
# Only return true if more than 5 samples have a square root greater than 2.0 (arbitrary)
extracted = self.extract(signal)
# If we want to filter out signals with fewer than 5 matching samples, then we
# should retrun True when there are 5 or more matching samples.
n_meeting_threshold = len(
extracted[extracted > self.threshold]
) # Number of samples greater than the threshold
meets_criteria = (
n_meeting_threshold >= 5
) # Are there at least 5 samples meeting this threshold?
return meets_criteria
def apply_feature_filters(capture: CaptureOrTimeSeries, filters: List[FilterPlugin]) -> bool:
"""
Check whether an array of current values (i.e. a single nanopore capture)
passes a set of filters. Filters can be based on summary statistics
(e.g., mean) and/or a range of allowed values.
Notes on filter behavior: If the filters list is empty, there are no filters
and the capture passes.
Parameters
----------
capture : CaptureOrTimeSeries | NumpyArrayLike
Capture containing time series of nanopore current values for a single capture, or the signal itself.
filters : List[FilterPlugin]
List of FilterPlugin instances. Write your own filter by subclassing FilterPlugin.
Returns
-------
boolean
True if capture passes all filters; False otherwise.
"""
if filters is None:
filters = []
# TODO: Parallelize? https://github.com/uwmisl/poretitioner/issues/67
filtered = [filter_out(capture) for filter_out in filters]
print(filtered)
# Did this signal pass all filters?
all_passed = all(filtered)
return all_passed
def check_capture_ejection_by_read(f5, read_id):
"""Checks whether the current capture was in the pore until the voltage
was reversed.
Parameters
----------
f5 : h5py.File object (open for reading or more)
Capture fast5 file
read_id : TODO
Returns
-------
boolean
True if the end of the capture coincides with the end of a voltage window.
"""
try:
ejected = f5.get(f"/read_{read_id}/Signal").attrs["ejected"]
except AttributeError:
raise ValueError(f"path /read_{read_id} does not exist in the fast5 file.")
return ejected
def check_capture_ejection(end_capture, voltage_ends, tol_obs=20):
"""Checks whether the current capture was in the pore until the voltage
was reversed.
Essentially checks whether a value (end_capture) is close enough (within
a margin of tol_obs) to any value in voltage_ends.
Parameters
----------
end_capture : numeric
The end time of the capture.
voltage_ends : list of numeric
List of times when the standard voltage ends.
tol_obs : int, optional
Tolerance for defining when the end of the capture = voltage end, by default 20
Returns
-------
boolean
True if the end of the capture coincides with the end of a voltage window.
"""
for voltage_end in voltage_ends:
if np.abs(end_capture - voltage_end) < tol_obs:
return True
return False
def filter_like_existing(config, example_fast5, example_filter_path, fast5_files, new_filter_path):
# Filters a set of fast5 files exactly the same as an existing filter
# TODO : #68 : implement
raise NotImplementedError()
def get_filter_pass_path(filter_set_id, read_id):
return FILTER_PATH.filter_set_pass_path(filter_set_id)
__DEFAULT_FILTER_PLUGINS = [
MeanFilter,
StandardDeviationFilter,
MedianFilter,
MinimumFilter,
MaximumFilter,
LengthFilter,
]
DEFAULT_FILTER_PLUGINS = {
filter_plugin_class.name(): filter_plugin_class
for filter_plugin_class in __DEFAULT_FILTER_PLUGINS
}
class Filtering(Protocol):
"""Classes that adhere to the Filtering protocol
provide an 'apply' method to an input that returns True
if and only if the input passes its filter.
These are also callable, so calling a filter on an input
is functionally equivalent to calling its apply method.
"""
def __call__(self, *args, **kwargs) -> bool:
raise NotImplementedError("Filtering protocol hasn't implemented __call__ yet!")
def apply(self, *args, **kwargs) -> bool:
raise NotImplementedError("Filtering protocol hasn't implemented Apply yet!")
@dataclass
class Filter(Filtering):
"""A named filter that can be applied to some data.
You can use this filter by just calling it on some data.
my_signal = [1,2,3,4]
filter = Filter(...)
passed_filter: bool = filter(my_signal)
Parameters
----------
config : FilterConfig
A description of this filter's configuration (e.g. where it was loaded from).
plugin : FilterPlugin
The actual implementation of this filter.
We have this class defined with
"""
config: FilterConfig
plugin: FilterPlugin
def __call__(self, *args, **kwargs) -> bool:
return self.plugin(*args, **kwargs)
def apply(self, *args, **kwargs) -> bool:
return self.plugin.apply(*args, **kwargs)
@property
def name(self) -> FilterName:
return FilterName(self.plugin.__class__.name())
def as_attr(self) -> Dict[str, Any]:
name = self.name
attrs = {**vars(self.config), **vars(self.plugin), name: name}
return attrs
def from_attr(self, attr) -> IsAttr:
...
import json
@dataclass
class HDF5_FilterSerialable(Filter, HDF5_GroupSerialableDataclass):
def as_group(self, parent_group: HDF5_Group, log: Optional[Logger] = None) -> HDF5_Group:
log = log if log is not None else getLogger()
# Note: This line simply registers a group with the name 'name' in the parent group.
this_group = HDF5_Group(parent_group.require_group(self.name))
all_attrs = {**self.config, **vars(self.plugin)}
this_group.create_attrs(all_attrs)
# Implementers must now write their serialized instance to this group.
return this_group
@classmethod
def from_group(
cls, group: HDF5_Group, log: Optional[Logger] = None
) -> HDF5_GroupSerialableDataclass:
# You see, the trouble is, in the above 'as_group' call, we lumped together
# all the attributes of the FilterConfig and the FilterPlugin, not knowing
# which attributes belonged to which class.
#
# Now, here in `from_group`, it's time to pay the piper and figure out which attribute
# goes where to create a new Filter instance.
#
# This is likely achievable through the plugin architecture, since the plugin's
# name is unique, we can try to find a plugin with a given name, then get its attributes from there.
# Load
log.warning("Filter.from_group not implemented...It's a whole thing (see comment)")
# This is pure <NAME>.
return super().from_group(group, log)
# class Filters(HDF5_GroupSerialableDataclass):
# filters:
Filters = Dict[FilterName, Filter]
def get_filters(filter_configs: Optional[FilterConfigs] = None) -> Filters:
"""Creates Filters from a list of filter configurations.
Parameters
----------
filter_configs : Optional[FilterConfigs]
A mapping of filter names to their configurations, None by default (i.e. no filtering).
Returns
-------
Filters
A set of callable/applyable filters.
"""
filter_configs = filter_configs if filter_configs is not None else FilterConfigs({})
my_filters = {
name: filter_from_config(name, filter_config)
for name, filter_config in filter_configs.items()
}
return my_filters
def does_pass_filters(capture: CaptureOrTimeSeries, filters: Iterable[Filter]) -> bool:
"""
Check whether an array of values (e.g. a single nanopore capture)
passes a set of filters. Filters can be based on summary statistics
(e.g., mean) and/or a range of allowed values.
Parameters
----------
capture : CaptureOrTimeSeries | NumpyArrayLike
Capture containing time series of nanopore current values for a single capture, or the signal itself.
filters : Iterable[Filter]
The set of filters to apply. Write your own filter by subclassing FilterPlugin.
Returns
-------
boolean
True if capture passes all filters; False otherwise.
"""
all_passed = True
for some_filter in filters:
if not some_filter(capture):
return False
return all_passed
@dataclass(frozen=True)
class FilterSetProtocol(Filtering, Protocol):
filter_set_id: FilterSetId
filters: Filters
@classmethod
def from_filter_configs(cls, name: FilterSetId, filter_configs: FilterConfigs = None):
...
@dataclass(frozen=True, init=False)
class FilterSet(FilterSetProtocol):
"""
A collection of filters with a name for easy
identification. Essentially a mapping of filter names to their implementations.
"""
def validate(self):
raise NotImplementedError("Implement validation for filters!")
def __init__(self, filter_set_id: FilterSetId, filters: Filters) -> None:
filterset = super().__init__(self)
object.__setattr__(self, "filter_set_id", filter_set_id)
object.__setattr__(self, "filters", filters)
# self.name = name
# self.filters = filters
############################
#
# FilterSetProtocol
#
############################
@classmethod
def from_filter_configs(cls, name: FilterSetId, filter_configs: FilterConfigs = None):
filters: Filters = get_filters(filter_configs)
filter_set = cls.__new__(cls, name, filters)
filter_set.__init__(name, filters)
return filter_set
def apply(self, capture: CaptureOrTimeSeries) -> bool:
return does_pass_filters(capture, self.filters.values())
def __call__(self, capture: CaptureOrTimeSeries) -> bool:
return self.apply(capture)
class HDF5_FilterSet(FilterSet, HDF5_GroupSerialableDataclass):
def __init__(self, filter_set: FilterSet) -> None:
self._filterset = filter_set
############################
#
# HDF5_GroupSerializable
#
############################
def name(self):
return self._filterset.filter_set_id
def as_group(self, parent_group: HDF5_Group, log: Optional[Logger] = None) -> HDF5_Group:
filter_set_group = parent_group.require_group(self.name())
for name, filter_t in self._filterset.filters.items():
hdf5_filter = HDF5_FilterSerialable(filter_t.config, filter_t.plugin)
hdf5_filter.as_group(filter_set_group)
return HDF5_Group(filter_set_group)
# @classmethod
# def from_group(
# cls, group: HDF5_Group, log: Optional[Logger] = None
# ) -> HDF5_GroupSerializable:
# raise NotImplementedError(
# f"from_group not implemented for {cls.__name__}. Make sure you write a method that returns a serialzied version of this object."
# )
def filter_from_config(name: str, config: FilterConfig, log: Logger = getLogger()) -> Filter:
"""Creates a Filter from a config spefication. If no "filename" is present in the FilterConfig, it's
assumed to be one of the default filtesr
Parameters
----------
name : str
The unique name of a filter.
config : FilterConfig
Filter configuration to build the plugin.
log : Logger, optional
Logger to use for information/warnings/debug, by default getLogger()
Returns
-------
Filter
A filter that can be applied to some data.
Raises
------
AttributeError
A filter plugin could not be built from the configuration description. If this error is raised, be sure to check
1) A plugin class with the name in the configuration is defined at the filepath described in the configuration
2) The plugin class inherits from the `FilterPlugin` abstract base class.
"""
filepath = config.get("filepath", None)
# TODO: For non-default FilterPlugins, load/unpickle the class from the filepath. https://github.com/uwmisl/poretitioner/issues/91
plugin = None
if name in DEFAULT_FILTER_PLUGINS:
plugin = DEFAULT_FILTER_PLUGINS[name]()
else:
# TODO: For non-default FilterPlugins, load the class from the filepath. https://github.com/uwmisl/poretitioner/issues/91
plugin = plugin_from_file(name, filepath)
pass
# Make sure any plugin attributes defined in the config are moved over to the plugin instance.
try:
# Here, we take care of setting whatever attributes the plugin config defines on the new plugin instance.
for key, value in config.items():
object.__setattr__(plugin, key, value)
except AttributeError as e:
log.warning(
"""
Uh oh, couldn't find plugin '{name}'. Are you sure:
1) A plugin class with the name '{name}' is defined in the file {filepath}?
2) That plugin class inherits from `FilterPlugin`?
"""
)
raise e
my_filter = Filter(config, plugin)
return my_filter
def plugin_from_file(name: str, filepath: PathLikeOrString):
"""[summary]
Parameters
----------
name : str
[description]
filepath : PathLikeOrString
[description]
Returns
-------
[type]
[description]
Raises
------
NotImplementedError
[description]
"""
# TODO: For non-default FilterPlugins, load/unpickle the class from the filepath. https://github.com/uwmisl/poretitioner/issues/91
raise NotImplementedError(
"Plugin from file has not been implemented! This method should take in a filepath and filter name, and return a runnable FilterPlugin!"
)
|
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1542,
703,
5072,
3991,
29879,
613,
360,
919,
29961,
5072,
1170,
29892,
19916,
3991,
2314,
13,
13,
29937,
14402,
29901,
19916,
1858,
3851,
881,
1423,
393,
1024,
338,
5412,
29889,
2045,
597,
3292,
29889,
510,
29914,
7262,
29885,
7497,
29914,
1971,
300,
654,
261,
29914,
12175,
29914,
29929,
29896,
13,
1990,
19916,
16288,
29898,
16288,
1125,
13,
1678,
9995,
13,
1678,
25513,
770,
363,
19916,
18224,
29889,
1763,
2436,
596,
1914,
4175,
29892,
19481,
445,
9846,
13,
1678,
770,
322,
2334,
278,
421,
7302,
29952,
1158,
322,
421,
978,
29952,
2875,
29889,
13,
1678,
9995,
13,
13,
1678,
732,
1990,
5696,
13,
1678,
732,
16595,
5696,
13,
1678,
822,
1024,
29898,
25932,
29897,
1599,
851,
29901,
13,
4706,
9995,
8110,
802,
1024,
363,
445,
4175,
29889,
13,
4706,
8561,
1854,
372,
1838,
29915,
29873,
14529,
411,
738,
5923,
2983,
29889,
13,
13,
4706,
16969,
13,
4706,
448,
22158,
13,
4706,
851,
13,
9651,
450,
5412,
1024,
363,
445,
4175,
313,
29872,
29889,
29887,
29889,
376,
29888,
283,
4336,
29918,
9067,
2564,
13,
13,
4706,
390,
1759,
267,
13,
4706,
448,
23648,
13,
4706,
2216,
1888,
2037,
287,
2392,
13,
9651,
390,
1759,
287,
565,
445,
4175,
338,
2000,
1728,
445,
1024,
1158,
1641,
8762,
29889,
13,
4706,
9995,
13,
4706,
12020,
2216,
1888,
2037,
287,
2392,
29898,
13,
9651,
13577,
978,
29915,
770,
1158,
451,
8762,
363,
4175,
29889,
910,
770,
1158,
881,
736,
263,
5412,
1024,
363,
445,
4175,
1213,
13,
4706,
1723,
13,
13,
1678,
732,
16595,
5696,
13,
1678,
822,
3394,
29898,
1311,
29892,
10446,
29901,
8868,
545,
2816,
2481,
19204,
29897,
1599,
6120,
29901,
13,
4706,
9995,
11609,
29879,
5852,
565,
263,
10446,
14517,
263,
2183,
4175,
16614,
29889,
13,
4706,
1152,
2777,
29892,
263,
3464,
4175,
723,
1423,
393,
263,
10446,
29915,
29879,
15837,
1002,
2879,
1199,
3804,
2629,
263,
2183,
3464,
29889,
13,
13,
4706,
12662,
2699,
13,
4706,
448,
1378,
29899,
13,
4706,
10446,
584,
7442,
29889,
1017,
15702,
29889,
2588,
27552,
13,
9651,
5974,
3652,
10446,
304,
4175,
29889,
13,
13,
4706,
16969,
13,
4706,
448,
22158,
13,
4706,
6120,
13,
9651,
26460,
445,
10446,
14517,
278,
4175,
29889,
13,
13,
4706,
390,
1759,
267,
13,
4706,
448,
23648,
13,
4706,
2216,
1888,
2037,
287,
2392,
13,
9651,
390,
1759,
287,
746,
278,
4175,
1158,
3508,
29915,
29873,
8762,
491,
278,
1136,
9929,
19916,
770,
13,
4706,
9995,
13,
4706,
12020,
2216,
1888,
2037,
287,
2392,
29898,
13,
9651,
13577,
7302,
29915,
1158,
451,
8762,
363,
4175,
29889,
910,
1158,
881,
736,
5852,
565,
322,
871,
565,
7436,
304,
263,
10446,
393,
28103,
278,
4175,
28770,
291,
29889,
1152,
2777,
29892,
376,
13,
4706,
1723,
13,
13,
1678,
822,
4770,
4804,
12035,
1311,
29892,
10446,
29901,
8868,
545,
2816,
2481,
19204,
29897,
1599,
6120,
29901,
13,
4706,
9995,
2052,
368,
278,
4175,
29889,
13,
13,
4706,
5282,
2827,
21326,
4804,
1649,
29952,
16869,
502,
437,
7575,
2712,
763,
29901,
13,
13,
4706,
770,
1619,
7281,
5072,
29898,
5072,
16288,
1125,
13,
9651,
822,
3394,
29898,
17885,
545,
1125,
13,
18884,
396,
2023,
13,
18884,
1209,
13,
13,
4706,
396,
12699,
297,
775,
988,
21166,
338,
2309,
3045,
13,
13,
4706,
2854,
29918,
17885,
1973,
353,
5159,
13,
4706,
18094,
353,
518,
1619,
7281,
5072,
3285,
7280,
7281,
5072,
3285,
2023,
4514,
13,
13,
4706,
2854,
29918,
17885,
1973,
353,
518,
17885,
545,
363,
10446,
297,
4332,
1973,
565,
599,
4197,
1777,
29873,
29898,
17885,
545,
29897,
363,
977,
29873,
297,
18094,
2314,
29962,
13,
4706,
363,
10446,
297,
4332,
1973,
29901,
396,
887,
29915,
29881,
864,
304,
8943,
675,
445,
297,
263,
1855,
2834,
1342,
856,
13,
13,
9651,
363,
977,
29873,
297,
18094,
29901,
13,
18884,
22289,
29918,
17885,
1973,
353,
977,
29873,
29898,
17885,
545,
467,
13,
13,
4706,
12662,
2699,
13,
4706,
448,
1378,
29899,
13,
4706,
10446,
584,
8868,
545,
2816,
2481,
19204,
13,
9651,
8868,
545,
304,
4175,
29889,
13,
13,
4706,
16969,
13,
4706,
448,
22158,
13,
4706,
6120,
13,
9651,
26460,
445,
10446,
14517,
278,
4175,
29889,
13,
4706,
9995,
13,
4706,
1121,
353,
1583,
29889,
7302,
29898,
17885,
545,
29897,
13,
4706,
736,
1121,
13,
13,
13,
29934,
24336,
29918,
3738,
29931,
4945,
29918,
23397,
29918,
16173,
7833,
5005,
29901,
5785,
353,
448,
9302,
29889,
7192,
13,
29934,
24336,
29918,
3738,
29931,
4945,
29918,
23397,
29918,
12648,
7833,
5005,
29901,
5785,
353,
7442,
29889,
7192,
13,
13,
13,
1990,
12146,
5072,
29898,
5072,
16288,
1125,
13,
1678,
822,
4770,
2344,
12035,
1311,
29892,
9212,
29901,
28379,
29961,
7411,
29962,
353,
6213,
29892,
7472,
29901,
28379,
29961,
7411,
29962,
353,
6213,
1125,
13,
4706,
9995,
29909,
4175,
393,
18094,
2729,
373,
3692,
263,
7182,
20074,
1546,
263,
7472,
322,
263,
9212,
29889,
13,
13,
4706,
12662,
2699,
13,
4706,
448,
1378,
29899,
13,
4706,
9212,
584,
5785,
29892,
13136,
13,
9651,
450,
19087,
995,
445,
7182,
881,
367,
6068,
304,
2125,
313,
262,
7009,
573,
511,
491,
2322,
12146,
5072,
29889,
23397,
29918,
16173,
7833,
5005,
13,
4706,
7472,
584,
5785,
29892,
13136,
13,
9651,
450,
10150,
995,
445,
7182,
881,
367,
6068,
304,
2125,
313,
262,
7009,
573,
511,
491,
2322,
12146,
5072,
29889,
23397,
29918,
12648,
7833,
5005,
13,
4706,
9995,
13,
4706,
1583,
29889,
1195,
12539,
353,
9212,
565,
9212,
338,
451,
6213,
1683,
390,
24336,
29918,
3738,
29931,
4945,
29918,
23397,
29918,
16173,
7833,
5005,
13,
4706,
1583,
29889,
27525,
398,
353,
7472,
565,
7472,
338,
451,
6213,
1683,
390,
24336,
29918,
3738,
29931,
4945,
29918,
23397,
29918,
12648,
7833,
5005,
13,
13,
1678,
822,
6597,
29898,
1311,
29892,
10446,
29901,
8868,
545,
2816,
2481,
19204,
29897,
1599,
11848,
2272,
2588,
27552,
29901,
13,
4706,
9995,
5647,
1461,
29879,
263,
15837,
1002,
4695,
515,
278,
10446,
313,
29872,
29889,
29887,
29889,
2099,
29892,
3309,
29892,
3918,
29522,
467,
13,
13,
4706,
27486,
5858,
491,
2322,
313,
5143,
3639,
278,
10446,
467,
13,
13,
4706,
887,
508,
671,
445,
740,
304,
4327,
278,
848,
297,
263,
5407,
982,
1434,
9068,
372,
313,
29872,
29889,
29887,
29889,
13,
4706,
2805,
278,
2099,
995,
310,
263,
10446,
1434,
21166,
2729,
373,
393,
2099,
1846,
13,
13,
4706,
3940,
29901,
960,
591,
7623,
278,
21166,
27321,
408,
385,
382,
14632,
313,
5647,
1461,
29899,
13372,
29899,
5896,
29897,
16439,
29892,
445,
723,
367,
278,
376,
9067,
29908,
13,
4706,
313,
19730,
848,
29892,
6623,
372,
363,
263,
2678,
6437,
511,
541,
306,
4459,
393,
376,
9067,
29908,
338,
6060,
263,
3984,
25369,
740,
1024,
297,
445,
3030,
29889,
13,
13,
4706,
12662,
2699,
13,
4706,
448,
1378,
29899,
13,
4706,
10446,
584,
8868,
545,
2816,
2481,
19204,
13,
9651,
8868,
545,
515,
607,
304,
6597,
848,
29889,
13,
4706,
9995,
13,
4706,
1018,
29901,
13,
9651,
7182,
353,
10446,
29889,
29888,
13857,
284,
1891,
580,
13,
4706,
5174,
23833,
2392,
29901,
13,
9651,
7182,
353,
10446,
13,
4706,
1683,
29901,
13,
9651,
7182,
353,
10446,
13,
4706,
736,
7182,
13,
4706,
396,
7182,
353,
679,
5552,
29898,
17885,
545,
29892,
8868,
545,
29889,
29888,
13857,
284,
1891,
17255,
978,
1649,
29892,
10446,
29897,
13,
13,
1678,
822,
338,
29918,
262,
29918,
3881,
29898,
1311,
29892,
995,
29901,
7761,
29961,
8009,
2272,
2588,
27552,
29892,
5785,
2314,
1599,
6120,
29901,
13,
4706,
1018,
29901,
13,
9651,
396,
960,
278,
995,
338,
925,
263,
5785,
29892,
591,
508,
671,
445,
1361,
29891,
5877,
29901,
13,
9651,
736,
1583,
29889,
1195,
12539,
5277,
995,
5277,
1583,
29889,
27525,
398,
13,
4706,
5174,
7865,
2392,
29901,
13,
9651,
396,
1205,
591,
29915,
276,
451,
6068,
304,
671,
393,
5877,
373,
12655,
7049,
29889,
13,
9651,
736,
599,
29898,
9302,
29889,
1188,
936,
29918,
392,
29898,
1311,
29889,
1195,
12539,
5277,
995,
29892,
995,
5277,
1583,
29889,
27525,
398,
876,
13,
13,
1678,
822,
3394,
29898,
1311,
29892,
7182,
1125,
13,
4706,
995,
353,
1583,
29889,
21111,
29898,
25436,
29897,
13,
4706,
736,
1583,
29889,
275,
29918,
262,
29918,
3881,
29898,
1767,
29897,
13,
13,
13,
1990,
10117,
2772,
14641,
5072,
29898,
6069,
5072,
1125,
13,
1678,
9995,
3434,
2153,
363,
4332,
1973,
411,
3918,
29668,
800,
297,
777,
3464,
1213,
15945,
13,
13,
1678,
732,
1990,
5696,
13,
1678,
822,
1024,
29898,
25932,
29897,
1599,
851,
29901,
13,
4706,
736,
376,
4172,
29894,
29908,
13,
13,
1678,
822,
6597,
29898,
1311,
29892,
10446,
29901,
8868,
545,
2816,
2481,
19204,
1125,
13,
4706,
7182,
353,
2428,
2141,
21111,
29898,
17885,
545,
29897,
13,
4706,
736,
7442,
29889,
4172,
29898,
25436,
29897,
13,
13,
13,
1990,
16316,
5072,
29898,
6069,
5072,
1125,
13,
1678,
9995,
3434,
2153,
363,
4332,
1973,
411,
385,
23342,
2099,
2629,
263,
3464,
1213,
15945,
13,
13,
1678,
732,
1990,
5696,
13,
1678,
822,
1024,
29898,
25932,
29897,
1599,
851,
29901,
13,
4706,
736,
376,
12676,
29908,
13,
13,
1678,
822,
6597,
29898,
1311,
29892,
10446,
29901,
8868,
545,
2816,
2481,
19204,
1125,
13,
4706,
7182,
353,
2428,
2141,
21111,
29898,
17885,
545,
29897,
13,
4706,
736,
7442,
29889,
12676,
29898,
25436,
29897,
13,
13,
13,
1990,
3436,
713,
5072,
29898,
6069,
5072,
1125,
13,
1678,
9995,
3434,
2153,
363,
4332,
1973,
411,
263,
19194,
2629,
263,
3464,
1213,
15945,
13,
13,
1678,
732,
1990,
5696,
13,
1678,
822,
1024,
29898,
25932,
29897,
1599,
851,
29901,
13,
4706,
736,
376,
2168,
713,
29908,
13,
13,
1678,
822,
6597,
29898,
1311,
29892,
10446,
29901,
8868,
545,
2816,
2481,
19204,
1125,
13,
4706,
7182,
353,
2428,
2141,
21111,
29898,
17885,
545,
29897,
13,
4706,
736,
7442,
29889,
2168,
713,
29898,
25436,
29897,
13,
13,
13,
1990,
3080,
12539,
5072,
29898,
6069,
5072,
1125,
13,
1678,
9995,
3434,
2153,
363,
4332,
1973,
411,
263,
9212,
2629,
263,
3464,
1213,
15945,
13,
13,
1678,
732,
1990,
5696,
13,
1678,
822,
1024,
29898,
25932,
29897,
1599,
851,
29901,
13,
4706,
736,
376,
1195,
29908,
13,
13,
1678,
822,
6597,
29898,
1311,
29892,
10446,
29901,
8868,
545,
2816,
2481,
19204,
1125,
13,
4706,
7182,
353,
2428,
2141,
21111,
29898,
17885,
545,
29897,
13,
4706,
736,
7442,
29889,
1195,
29898,
25436,
29897,
13,
13,
13,
1990,
5918,
12539,
5072,
29898,
6069,
5072,
1125,
13,
1678,
9995,
3434,
2153,
363,
4332,
1973,
411,
263,
7472,
2629,
263,
3464,
1213,
15945,
13,
13,
1678,
732,
1990,
5696,
13,
1678,
822,
1024,
29898,
25932,
29897,
1599,
851,
29901,
13,
4706,
736,
376,
3317,
29908,
13,
13,
1678,
822,
6597,
29898,
1311,
29892,
10446,
29901,
8868,
545,
2816,
2481,
19204,
1125,
13,
4706,
7182,
353,
2428,
2141,
21111,
29898,
17885,
545,
29897,
13,
4706,
736,
7442,
29889,
3317,
29898,
25436,
29897,
13,
13,
13,
1990,
365,
1477,
5072,
29898,
6069,
5072,
1125,
13,
1678,
9995,
3434,
2153,
4332,
1973,
2729,
373,
1009,
3309,
1213,
15945,
13,
13,
1678,
732,
1990,
5696,
13,
1678,
822,
1024,
29898,
25932,
29897,
1599,
851,
29901,
13,
4706,
736,
376,
2848,
29908,
13,
13,
1678,
822,
6597,
29898,
1311,
29892,
10446,
29901,
8868,
545,
2816,
2481,
19204,
1125,
13,
4706,
7182,
353,
2428,
2141,
21111,
29898,
17885,
545,
29897,
13,
4706,
736,
7431,
29898,
25436,
29897,
13,
13,
13,
1990,
382,
622,
287,
5072,
29898,
5072,
16288,
1125,
13,
1678,
9995,
3434,
2153,
4332,
1973,
2729,
373,
3692,
896,
892,
321,
622,
287,
515,
278,
282,
487,
1213,
15945,
13,
13,
1678,
732,
1990,
5696,
13,
1678,
822,
1024,
29898,
25932,
29897,
1599,
851,
29901,
13,
4706,
736,
376,
29872,
622,
287,
29908,
13,
13,
1678,
822,
6597,
29898,
1311,
29892,
10446,
29901,
8868,
545,
1125,
13,
4706,
736,
10446,
29889,
29872,
622,
287,
13,
13,
13,
15945,
29908,
13,
5328,
304,
6204,
3575,
438,
1233,
8701,
19916,
29901,
13,
13,
8139,
287,
901,
12862,
21166,
1135,
825,
591,
3867,
714,
310,
278,
3800,
29973,
1939,
1108,
29889,
13,
4391,
596,
1914,
2888,
4175,
491,
7846,
11407,
515,
278,
19916,
16288,
770,
29889,
13,
13,
2831,
445,
1342,
29892,
1235,
29915,
29879,
437,
1554,
4280,
29889,
14891,
366,
871,
864,
304,
25917,
4332,
1973,
13,
5747,
505,
901,
1135,
29871,
29945,
11916,
411,
263,
11266,
2095,
293,
18806,
296,
7621,
1135,
777,
16897,
29889,
13,
13,
7058,
2794,
1749,
2888,
4175,
29915,
29879,
421,
7302,
29952,
740,
881,
736,
5852,
565,
322,
871,
565,
13,
1552,
7182,
756,
901,
1135,
29871,
29945,
11916,
7621,
1135,
278,
16897,
29892,
1156,
5622,
278,
11266,
2095,
293,
18806,
296,
297,
421,
21111,
1412,
13,
15945,
29908,
13,
13,
13,
1990,
1619,
7281,
5072,
29898,
5072,
16288,
1125,
13,
1678,
16897,
29901,
5785,
353,
29871,
29900,
29889,
29945,
29871,
396,
19013,
635,
11472,
29889,
13,
13,
1678,
822,
1024,
29898,
1311,
1125,
13,
4706,
736,
376,
5431,
29908,
13,
13,
1678,
822,
6597,
29898,
1311,
29892,
10446,
1125,
13,
4706,
396,
1938,
278,
29304,
1244,
29892,
470,
758,
29899,
5014,
372,
1434,
278,
4175,
29889,
13,
13,
4706,
396,
402,
1691,
278,
11266,
2095,
293,
18806,
296,
310,
278,
7182,
29889,
13,
4706,
23892,
353,
7442,
29889,
13161,
29882,
29898,
17885,
545,
29889,
25436,
29897,
13,
4706,
736,
23892,
13,
13,
1678,
822,
3394,
29898,
1311,
29892,
7182,
1125,
13,
4706,
396,
9333,
736,
1565,
565,
901,
1135,
29871,
29945,
11916,
505,
263,
6862,
3876,
7621,
1135,
29871,
29906,
29889,
29900,
313,
279,
8844,
653,
29897,
13,
4706,
23892,
353,
1583,
29889,
21111,
29898,
25436,
29897,
13,
13,
4706,
396,
960,
591,
864,
304,
4175,
714,
18470,
411,
28145,
1135,
29871,
29945,
9686,
11916,
29892,
769,
591,
13,
4706,
396,
881,
5663,
348,
5852,
746,
727,
526,
29871,
29945,
470,
901,
9686,
11916,
29889,
13,
4706,
302,
29918,
1004,
15133,
29918,
386,
12268,
353,
7431,
29898,
13,
9651,
23892,
29961,
21111,
287,
1405,
1583,
29889,
386,
12268,
29962,
13,
4706,
1723,
29871,
396,
9681,
310,
11916,
7621,
1135,
278,
16897,
13,
4706,
28103,
29918,
29883,
21977,
353,
313,
13,
9651,
302,
29918,
1004,
15133,
29918,
386,
12268,
6736,
29871,
29945,
13,
4706,
1723,
29871,
396,
4683,
727,
472,
3203,
29871,
29945,
11916,
11781,
445,
16897,
29973,
13,
4706,
736,
28103,
29918,
29883,
21977,
13,
13,
13,
1753,
3394,
29918,
14394,
29918,
26705,
29898,
17885,
545,
29901,
8868,
545,
2816,
2481,
19204,
29892,
18094,
29901,
2391,
29961,
5072,
16288,
2314,
1599,
6120,
29901,
13,
1678,
9995,
13,
1678,
5399,
3692,
385,
1409,
310,
1857,
1819,
313,
29875,
29889,
29872,
29889,
263,
2323,
23432,
459,
487,
10446,
29897,
13,
1678,
14517,
263,
731,
310,
18094,
29889,
2514,
2153,
508,
367,
2729,
373,
15837,
13964,
13,
1678,
313,
29872,
29889,
29887,
1696,
2099,
29897,
322,
29914,
272,
263,
3464,
310,
6068,
1819,
29889,
13,
13,
1678,
8695,
373,
4175,
6030,
29901,
960,
278,
18094,
1051,
338,
4069,
29892,
727,
526,
694,
18094,
13,
1678,
322,
278,
10446,
14517,
29889,
13,
13,
1678,
12662,
2699,
13,
1678,
448,
1378,
29899,
13,
1678,
10446,
584,
8868,
545,
2816,
2481,
19204,
891,
11848,
2272,
2588,
27552,
13,
4706,
8868,
545,
6943,
931,
3652,
310,
23432,
459,
487,
1857,
1819,
363,
263,
2323,
10446,
29892,
470,
278,
7182,
3528,
29889,
13,
1678,
18094,
584,
2391,
29961,
5072,
16288,
29962,
13,
4706,
2391,
310,
19916,
16288,
8871,
29889,
14350,
596,
1914,
4175,
491,
19481,
292,
19916,
16288,
29889,
13,
13,
1678,
16969,
13,
1678,
448,
22158,
13,
1678,
7223,
13,
4706,
5852,
565,
10446,
14517,
599,
18094,
29936,
7700,
6467,
29889,
13,
1678,
9995,
13,
1678,
565,
18094,
338,
6213,
29901,
13,
4706,
18094,
353,
5159,
13,
13,
1678,
396,
14402,
29901,
1459,
6553,
675,
29973,
2045,
597,
3292,
29889,
510,
29914,
7262,
29885,
7497,
29914,
1971,
300,
654,
261,
29914,
12175,
29914,
29953,
29955,
13,
1678,
22289,
353,
518,
4572,
29918,
449,
29898,
17885,
545,
29897,
363,
4175,
29918,
449,
297,
18094,
29962,
13,
1678,
1596,
29898,
4572,
287,
29897,
13,
13,
1678,
396,
7440,
445,
7182,
1209,
599,
18094,
29973,
13,
1678,
599,
29918,
3364,
287,
353,
599,
29898,
4572,
287,
29897,
13,
1678,
736,
599,
29918,
3364,
287,
13,
13,
13,
1753,
1423,
29918,
17885,
545,
29918,
29872,
6929,
29918,
1609,
29918,
949,
29898,
29888,
29945,
29892,
1303,
29918,
333,
1125,
13,
1678,
9995,
5596,
29879,
3692,
278,
1857,
10446,
471,
297,
278,
282,
487,
2745,
278,
11749,
13,
1678,
471,
18764,
287,
29889,
13,
13,
1678,
12662,
2699,
13,
1678,
448,
1378,
29899,
13,
1678,
285,
29945,
584,
298,
29945,
2272,
29889,
2283,
1203,
313,
3150,
363,
5183,
470,
901,
29897,
13,
4706,
8868,
545,
5172,
29945,
934,
13,
1678,
1303,
29918,
333,
584,
14402,
13,
13,
1678,
16969,
13,
1678,
448,
22158,
13,
1678,
7223,
13,
4706,
5852,
565,
278,
1095,
310,
278,
10446,
22819,
2247,
411,
278,
1095,
310,
263,
11749,
3474,
29889,
13,
1678,
9995,
13,
1678,
1018,
29901,
13,
4706,
321,
622,
287,
353,
285,
29945,
29889,
657,
29898,
29888,
23901,
949,
648,
949,
29918,
333,
6822,
10140,
284,
2564,
5552,
29879,
3366,
29872,
622,
287,
3108,
13,
1678,
5174,
23833,
2392,
29901,
13,
4706,
12020,
7865,
2392,
29898,
29888,
29908,
2084,
847,
949,
648,
949,
29918,
333,
29913,
947,
451,
1863,
297,
278,
5172,
29945,
934,
23157,
13,
1678,
736,
321,
622,
287,
13,
13,
13,
1753,
1423,
29918,
17885,
545,
29918,
29872,
6929,
29898,
355,
29918,
17885,
545,
29892,
11749,
29918,
1975,
29892,
304,
29880,
29918,
26290,
29922,
29906,
29900,
1125,
13,
1678,
9995,
5596,
29879,
3692,
278,
1857,
10446,
471,
297,
278,
282,
487,
2745,
278,
11749,
13,
1678,
471,
18764,
287,
29889,
13,
13,
1678,
11044,
9247,
12747,
3692,
263,
995,
313,
355,
29918,
17885,
545,
29897,
338,
3802,
3307,
313,
2541,
262,
13,
1678,
263,
5906,
310,
304,
29880,
29918,
26290,
29897,
304,
738,
995,
297,
11749,
29918,
1975,
29889,
13,
13,
1678,
12662,
2699,
13,
1678,
448,
1378,
29899,
13,
1678,
1095,
29918,
17885,
545,
584,
16985,
13,
4706,
450,
1095,
931,
310,
278,
10446,
29889,
13,
1678,
11749,
29918,
1975,
584,
1051,
310,
16985,
13,
4706,
2391,
310,
3064,
746,
278,
3918,
11749,
10614,
29889,
13,
1678,
304,
29880,
29918,
26290,
584,
938,
29892,
13136,
13,
4706,
16977,
261,
749,
363,
16184,
746,
278,
1095,
310,
278,
10446,
353,
11749,
1095,
29892,
491,
2322,
29871,
29906,
29900,
13,
13,
1678,
16969,
13,
1678,
448,
22158,
13,
1678,
7223,
13,
4706,
5852,
565,
278,
1095,
310,
278,
10446,
22819,
2247,
411,
278,
1095,
310,
263,
11749,
3474,
29889,
13,
1678,
9995,
13,
1678,
363,
11749,
29918,
355,
297,
11749,
29918,
1975,
29901,
13,
4706,
565,
7442,
29889,
6897,
29898,
355,
29918,
17885,
545,
448,
11749,
29918,
355,
29897,
529,
304,
29880,
29918,
26290,
29901,
13,
9651,
736,
5852,
13,
1678,
736,
7700,
13,
13,
13,
1753,
4175,
29918,
4561,
29918,
735,
15423,
29898,
2917,
29892,
1342,
29918,
11255,
29945,
29892,
1342,
29918,
4572,
29918,
2084,
29892,
5172,
29945,
29918,
5325,
29892,
716,
29918,
4572,
29918,
2084,
1125,
13,
1678,
396,
2514,
2153,
263,
731,
310,
5172,
29945,
2066,
3721,
278,
1021,
408,
385,
5923,
4175,
13,
1678,
396,
14402,
584,
396,
29953,
29947,
584,
2334,
13,
1678,
12020,
2216,
1888,
2037,
287,
2392,
580,
13,
13,
13,
1753,
679,
29918,
4572,
29918,
3364,
29918,
2084,
29898,
4572,
29918,
842,
29918,
333,
29892,
1303,
29918,
333,
1125,
13,
1678,
736,
383,
6227,
4945,
29918,
10145,
29889,
4572,
29918,
842,
29918,
3364,
29918,
2084,
29898,
4572,
29918,
842,
29918,
333,
29897,
13,
13,
13,
1649,
23397,
29918,
3738,
29931,
4945,
29918,
7390,
23338,
1177,
29903,
353,
518,
13,
1678,
16316,
5072,
29892,
13,
1678,
10117,
2772,
14641,
5072,
29892,
13,
1678,
3436,
713,
5072,
29892,
13,
1678,
3080,
12539,
5072,
29892,
13,
1678,
5918,
12539,
5072,
29892,
13,
1678,
365,
1477,
5072,
29892,
13,
29962,
13,
13,
23397,
29918,
3738,
29931,
4945,
29918,
7390,
23338,
1177,
29903,
353,
426,
13,
1678,
4175,
29918,
8582,
29918,
1990,
29889,
978,
7295,
4175,
29918,
8582,
29918,
1990,
13,
1678,
363,
4175,
29918,
8582,
29918,
1990,
297,
4770,
23397,
29918,
3738,
29931,
4945,
29918,
7390,
23338,
1177,
29903,
13,
29913,
13,
13,
13,
1990,
19916,
292,
29898,
17830,
1125,
13,
1678,
9995,
27403,
393,
594,
4150,
304,
278,
19916,
292,
9608,
13,
1678,
3867,
385,
525,
7302,
29915,
1158,
304,
385,
1881,
393,
3639,
5852,
13,
1678,
565,
322,
871,
565,
278,
1881,
14517,
967,
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13,
13,
1678,
4525,
526,
884,
1246,
519,
29892,
577,
5432,
263,
4175,
373,
385,
1881,
13,
1678,
338,
740,
635,
7126,
304,
5432,
967,
3394,
1158,
29889,
13,
1678,
9995,
13,
13,
1678,
822,
4770,
4804,
12035,
1311,
29892,
334,
5085,
29892,
3579,
19290,
29897,
1599,
6120,
29901,
13,
4706,
12020,
2216,
1888,
2037,
287,
2392,
703,
5072,
292,
9608,
22602,
29915,
29873,
8762,
4770,
4804,
1649,
3447,
29991,
1159,
13,
13,
1678,
822,
3394,
29898,
1311,
29892,
334,
5085,
29892,
3579,
19290,
29897,
1599,
6120,
29901,
13,
4706,
12020,
2216,
1888,
2037,
287,
2392,
703,
5072,
292,
9608,
22602,
29915,
29873,
8762,
2401,
368,
3447,
29991,
1159,
13,
13,
13,
29992,
1272,
1990,
13,
1990,
19916,
29898,
5072,
292,
1125,
13,
1678,
9995,
29909,
4257,
4175,
393,
508,
367,
7436,
304,
777,
848,
29889,
13,
13,
1678,
887,
508,
671,
445,
4175,
491,
925,
5432,
372,
373,
777,
848,
29889,
13,
13,
1678,
590,
29918,
25436,
353,
518,
29896,
29892,
29906,
29892,
29941,
29892,
29946,
29962,
13,
13,
1678,
4175,
353,
19916,
29077,
13,
13,
1678,
4502,
29918,
4572,
29901,
6120,
353,
4175,
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] |
foreman/data_refinery_foreman/foreman/management/commands/test_import_external_sample_attributes.py
|
AlexsLemonade/refinebio
| 106 |
63570
|
from unittest.mock import patch
from django.test import TestCase
import vcr
from data_refinery_common.models import (
Contribution,
Experiment,
ExperimentSampleAssociation,
OntologyTerm,
Sample,
SampleAttribute,
)
from data_refinery_foreman.foreman.management.commands.import_external_sample_attributes import (
Command,
import_metadata,
import_sample_attributes,
)
TEST_METADATA = "/home/user/data_store/externally_supplied_metadata/test_data/metadata.json"
class ImportExternalSampleAttributesTestCase(TestCase):
def setUp(self):
experiment = Experiment()
experiment.accession_code = "GSE000"
experiment.alternate_accession_code = "E-GEOD-000"
experiment.title = "NONONONO"
experiment.description = "Boooooourns. Wasabi."
experiment.technology = "RNA-SEQ"
experiment.save()
self.experiment = experiment
# Create some samples to attach metadata to
sample = Sample()
sample.accession_code = "SRR123"
sample.technology = "RNA-SEQ"
sample.source_database = "SRA"
sample.title = "Not important"
sample.save()
experiment_sample_association = ExperimentSampleAssociation()
experiment_sample_association.sample = sample
experiment_sample_association.experiment = experiment
experiment_sample_association.save()
sample2 = Sample()
sample2.accession_code = "SRR456"
sample2.technology = "RNA-SEQ"
sample2.source_database = "SRA"
sample2.title = "Not important"
sample2.save()
experiment_sample_association = ExperimentSampleAssociation()
experiment_sample_association.sample = sample2
experiment_sample_association.experiment = experiment
experiment_sample_association.save()
# Create the ontology terms I'm using in the tests
name = OntologyTerm()
name.ontology_term = "PATO:0000122"
name.human_readable_name = "length"
name.save()
unit = OntologyTerm()
unit.ontology_term = "UO:0010012"
unit.human_readable_name = "thou"
unit.save()
contribution = Contribution()
contribution.source_name = "refinebio_tests"
contribution.methods_url = "ccdatalab.org"
contribution.save()
self.contribution = contribution
#
# Test import_sample_attributes()
#
def test_skip_unknown_sample(self):
"""Make sure that if someone has metadata for a sample that we haven't
surveyed then we just do nothing"""
METADATA = [{"PATO:0000122": {"value": 25, "unit": "UO:0010012"}}]
import_sample_attributes("SRR789", METADATA, self.contribution)
self.assertEqual(SampleAttribute.objects.all().count(), 0)
def test_import_invalid_ontology_term(self):
METADATA = [{"PATO:0000122": {"value": 25, "unit": "thou"}}]
self.assertRaises(
ValueError, import_sample_attributes, "SRR123", METADATA, self.contribution
)
METADATA = [{"length": {"value": 25, "unit": "UO:0010012"}}]
self.assertRaises(
ValueError, import_sample_attributes, "SRR123", METADATA, self.contribution
)
def test_import_valid_sample_attributes(self):
METADATA = [{"PATO:0000122": {"value": 25, "unit": "UO:0010012"}}]
import_sample_attributes("SRR123", METADATA, self.contribution)
self.assertEqual(SampleAttribute.objects.all().count(), 1)
contributed_metadata = Sample.objects.get(accession_code="SRR123").contributed_metadata
self.assertEqual(
contributed_metadata[self.contribution.source_name]["length"],
{"unit": "thou", "value": 25},
)
#
# Test import_metadata()
#
def test_import_valid_metadata(self):
METADATA = [
{
"sample_accession": "SRR123",
"attributes": [{"PATO:0000122": {"value": 25, "unit": "UO:0010012"}}],
}
]
import_metadata(METADATA, self.contribution)
self.assertEqual(SampleAttribute.objects.all().count(), 1)
contributed_metadata = Sample.objects.get(accession_code="SRR123").contributed_metadata
self.assertEqual(
contributed_metadata[self.contribution.source_name]["length"],
{"unit": "thou", "value": 25},
)
#
# End-to-end test
#
@vcr.use_cassette("/home/user/data_store/cassettes/foreman.sample_attributes.end-to-end.yaml")
def test_management_command(self):
sample = Sample()
sample.accession_code = "DRR001173"
sample.technology = "RNA-SEQ"
sample.source_database = "SRA"
sample.title = "Not important"
sample.save()
command = Command()
SOURCE_NAME = "refinebio_tests"
command.handle(file=TEST_METADATA, source_name=SOURCE_NAME, methods_url="ccdatalab.org")
self.assertEqual(SampleAttribute.objects.all().count(), 1)
contributed_metadata = sample.contributed_metadata
self.assertEqual(
set(contributed_metadata[SOURCE_NAME]["biological sex"].keys()),
{"value", "confidence"},
)
self.assertEqual(
contributed_metadata[SOURCE_NAME]["biological sex"]["value"].human_readable_name,
"female",
)
self.assertAlmostEqual(
contributed_metadata[SOURCE_NAME]["biological sex"]["confidence"], 0.7856624891880539
)
|
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] |
src/Books/book.py
|
cclauss/pydantic-books-example
| 4 |
83224
|
<reponame>cclauss/pydantic-books-example<gh_stars>1-10
from __future__ import annotations
from datetime import date
from typing import Optional
from pydantic import BaseModel, validator
class Author(BaseModel):
name: str
@validator("name")
def author_name_must_not_be_an_empty_string(cls, v):
if not v:
raise ValueError("name must not be an empty string")
return v
class Publisher(BaseModel):
name: str
@validator("name")
def publisher_name_must_not_be_an_empty_string(cls, v):
if not v:
raise ValueError("name must not be an empty string")
return v
class SourceRecord(BaseModel):
record: str
@validator("record")
def record_must_not_be_an_empty_string(cls, v):
if not v:
raise ValueError("record must not be an empty string")
return v
class Book(BaseModel):
title: str
source_records: list[SourceRecord]
authors: list[Author]
publishers: list[Publisher]
publish_date: Optional[date]
author: str
publisher: str
@validator("source_records", "authors", "publishers")
def list_must_not_be_empty(cls, v):
if not v:
raise ValueError("list must not be empty")
return v
# doctests... to be run with `pytest --doctest-modules`
def doctest_author():
"""
>>> Author() # Must provide a `name` parameter
Traceback (most recent call last):
...
pydantic.error_wrappers.ValidationError: 1 validation error for Author
name
field required (type=value_error.missing)
>>> Author(name="") # @validator() ensures that an empty string is not allowed
Traceback (most recent call last):
...
pydantic.error_wrappers.ValidationError: 1 validation error for Author
name
name must not be an empty string (type=value_error)
>>> Author("") # `name =` is NOT optional
Traceback (most recent call last):
...
TypeError: __init__() takes exactly 1 positional argument (2 given)
>>> Author(name="Bob")
Author(name='Bob')
>>> external_data = {"name": "<NAME>"}
>>> from_dict = Author(**external_data) # Create an Author from a dict
>>> from_dict
Author(name='<NAME>')
>>> from_dict.json() # Pydantic has json.dumps() is builtin
'{"name": "<NAME>"}'
"""
def doctest_book():
"""
>>> Book(
... title = "",
... source_records = [],
... authors = [],
... publishers = [],
... publish_date = None,
... author = "",
... publisher = "",
... )
Traceback (most recent call last):
...
pydantic.error_wrappers.ValidationError: 3 validation errors for Book
source_records
list must not be empty (type=value_error)
authors
list must not be empty (type=value_error)
publishers
list must not be empty (type=value_error)
>>> Book(
... title = "",
... source_records = [Author(name="Bob")],
... authors = [Author(name="Bob")],
... publishers = [Author(name="Bob")],
... publish_date = None,
... author = "",
... publisher = "",
... )
Traceback (most recent call last):
...
pydantic.error_wrappers.ValidationError: 1 validation error for Book
source_records -> 0 -> record
field required (type=value_error.missing)
>>> Book(
... title = "",
... source_records = [SourceRecord(record="record")],
... authors = [Author(name="Bob")],
... publishers = [Publisher(name="Bob's Publishing")],
... publish_date = None,
... author = "",
... publisher = "",
... )
Book(title='', source_records=[SourceRecord(record='record')], authors=[Author(name='Bob')], publishers=[Publisher(name="Bob's Publishing")], publish_date=None, author='', publisher='')
"""
def doctest_dates():
"""
https://pydantic-docs.helpmanual.io/usage/types/#datetime-types
>>> class DateHack(BaseModel):
... publish_date: date
...
>>> DateHack(publish_date="2020-01-01")
DateHack(publish_date=datetime.date(2020, 1, 1))
>>> DateHack(publish_date=1577833200)
DateHack(publish_date=datetime.date(2019, 12, 31))
>>> DateHack(publish_date="1/1/2020")
Traceback (most recent call last):
...
pydantic.error_wrappers.ValidationError: 1 validation error for DateHack
publish_date
invalid date format (type=value_error.date)
>>> DateHack(publish_date="January, 2020")
Traceback (most recent call last):
...
pydantic.error_wrappers.ValidationError: 1 validation error for DateHack
publish_date
invalid date format (type=value_error.date)
"""
|
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] |
code/socialDistribution/models/author.py
|
CMPUT404F21TEAM/social-distribution
| 0 |
187373
|
from django.db import models
from django.contrib.auth.models import User
from django.utils import timezone
import datetime
import uuid
from cmput404.constants import API_BASE, STRING_MAXLEN, URL_MAXLEN
from .follow import Follow
class Author(models.Model):
""" Author model which represents all authors.
All authors that interact with the application will be stored as an author. That is,
the Author model can store both remote and local authors. Methods on an Author instance primarily
rely on API calls to get the data corresponding to the author from a remote server.
In the future, caching can be used with this model to reduce the number of API calls being sent out.
When a local author is created, it must be re-fetched from the database in order to access the auto-generated author.url attribute.
"""
# Django Software Foundation, https://docs.djangoproject.com/en/dev/ref/models/fields/#uuidfield
id = models.UUIDField(primary_key=True, default=uuid.uuid4, editable=False)
url = models.URLField(max_length=URL_MAXLEN)
host = models.URLField(max_length=STRING_MAXLEN, blank=True)
displayName = models.CharField(max_length=STRING_MAXLEN, default="Anonymous User")
githubUrl = models.CharField(max_length=URL_MAXLEN, null=True)
profileImageUrl = models.CharField(max_length=URL_MAXLEN, null=True)
created_date = models.DateTimeField(auto_now_add=True)
# timestamp of when the object was last updated
# will need later for caching
_last_updated = models.DateTimeField(auto_now=True)
# true means that field data will always be up-to-date (used by LocalAuthor)
_always_up_to_date = models.BooleanField(default=False)
def get_url_id(self):
""" Returns the id of the author in url form """
return self.url
def has_follow_request(self, author):
""" Over-ridden in LocalAuthor. Always returns False """
# No endpoint give us follow request objects
# Even if we GET against their inbox, we'll get back a list of posts
# The alternative would be to keep track of outgoing follow requests
return False
def get_inbox(self):
""" Gets the URL of the Authors inbox. """
return self.url.strip("/") + "/inbox/"
def as_json(self):
""" GET JSON representation of author
"""
json_data = {
"type": "author",
"id": self.url,
"host": self.host,
"displayName": self.displayName,
"url": self.url,
"github": self.githubUrl,
"profileImage": self.profileImageUrl
}
return json_data
def update_with_json(self, data):
'''
Add or update Author model data
Had to move here from utility.py due to import errors
'''
try:
if data.get("displayName"):
self.displayName = data['displayName']
if data.get('host'):
self.host = data['host']
if data.get("github"):
self.githubUrl = data['github']
if data.get("profileImage"):
self.profileImageUrl = data['profileImage']
self.save()
except:
pass
def up_to_date(self):
""" Checks if the author data is currently up do date. Returns true if either the
data is always maintained up-to-date or if the data was recently refreshed
"""
limit = timezone.now()-datetime.timedelta(seconds=6) # 6 seconds ago
was_recent_update = self._last_updated > limit
# return true if always up-to-date or if just updated
return self._always_up_to_date or was_recent_update
def __str__(self) -> str:
return f"Author: {self.url}"
class LocalAuthor(Author):
''' LocalAuthor model which represents an author hosted on the local server.
Any authors hosted on the local server will be stored as a LocalAuthor instance. This class overrides
methods defined in Author to provide more efficient, local access (no API) to author data.
LocalAuthor model:
user Author's corresponding Django User (text)
username Author's username (text)
displayName Author's displayName (text)
githubUrl Author's github url (text)
profileImageUrl Author's profile image url (text)
posts Posts created by the author
follows Followers of the author (Collection of Follow objects)
friend_requests Authors who have requested to follow author (Collection of LocalAuthor objects)
inbox_posts Posts sent to the inbox of the author
'''
user = models.OneToOneField(User, null=True, on_delete=models.CASCADE)
username = models.CharField(max_length=STRING_MAXLEN, unique=True, blank=False)
follow_requests = models.ManyToManyField('Author', related_name="sent_follow_requests")
inbox_posts = models.ManyToManyField('InboxPost')
def is_following(self, author: Author):
""" Returns true if self is following author, false otherwise. """
try:
self.following.get(object=author)
return True
except Follow.DoesNotExist:
return False
def has_follower(self, author: Author):
""" Returns true if author is a follower of self, false otherwise. """
try:
self.follows.get(actor=author)
return True
except Follow.DoesNotExist:
return False
def has_friend(self, author: Author):
""" Returns true if author is a friend of self, false otherwise. """
return Follow.are_friends(self, author)
def has_follow_request(self, author: Author):
""" Returns true if self has a follow request from author, false otherwise. """
return self.follow_requests.filter(id=author.id).exists()
def handle_follow_request(self, author: Author, accept: bool):
""" Removes author from follow requests (if exists) and resolves the request. If accept is
true, they are added as a follower of self.
"""
query_result = self.follow_requests.filter(id=author.id)
if query_result.exists():
self.follow_requests.remove(query_result.first())
if accept == True:
self.follows.get_or_create(actor=author)
def get_followers(self):
""" Gets the Authors who are followers of self. """
follows = self.follows.all()
followers = [f.actor for f in follows]
return followers
def get_friends(self):
""" Gets the Authors who are friends of self. """
follows = self.follows.all()
friends = [f.actor for f in follows if self.has_friend(f.actor)]
return friends
def __str__(self):
return self.displayName
def save(self, *args, **kwargs):
self._always_up_to_date = True
super().save(*args, **kwargs) # Call the "real" save() method.
self._set_url()
def _set_url(self):
""" Sets the URL of the author to be "http://{HOST}/{API_PREFIX}/author/{self.id}" if one was not set when created. This
sets the URL attribute of all local authors.
"""
# Clark, https://stackoverflow.com/users/10424244/clark, "Django - How to get self.id when saving a new object?",
# 2021-02-19, https://stackoverflow.com/a/66271445, CC BY-SA 4.0
url = f"{API_BASE}/author/{self.id}"
host = f"{API_BASE}/"
if self.url != url or self.host != host:
Author.objects.filter(id=self.id).update(url=url, host=host)
|
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reports/migrations/0005_moneyback_payment_system.py
|
Igorishe/Report_Traker
| 0 |
147191
|
<filename>reports/migrations/0005_moneyback_payment_system.py<gh_stars>0
# Generated by Django 3.2.3 on 2021-09-13 09:54
from django.db import migrations, models
class Migration(migrations.Migration):
dependencies = [
('reports', '0004_alter_moneyback_wallet'),
]
operations = [
migrations.AddField(
model_name='moneyback',
name='payment_system',
field=models.CharField(choices=[('Bitcoin', 'Bitcoin'), ('Ethereum', 'Ethereum'), ('WebMoney', 'Webmoney'), ('Qiwi', 'Qiwi')], default='Bitcoin', max_length=20, verbose_name='Платежная система'),
),
]
|
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] |
Spark/spark_media_CP.py
|
Dielam/Dielam.github.io
| 0 |
1612011
|
<filename>Spark/spark_media_CP.py
#!/usr/bin/python
#<NAME>
from pyspark import SparkConf, SparkContext
from shutil import rmtree
import string
import sys
import os
import os.path as path
if path.exists("output"):
rmtree("output")
if len(sys.argv) <= 1:
print("Error. No ha introducido Codigo Postal.")
else:
if len(sys.argv[1]) > 5:
print("Error. No ha introducido un Codigo Postal correcto.")
else:
#Spark configuration
conf = SparkConf().setAppName('AveragePriceCP')
sc = SparkContext(conf=conf)
RDDvar = sc.textFile("Gasolineras.csv")
wanted = sys.argv[1]
# CP = [6]
decode = RDDvar.map(lambda line: line.encode("ascii", "ignore"))
text = decode.filter(lambda line: wanted == line.split(',')[6])
# precio_gasolina_95 = [11]
data = text.filter(lambda line: str(line.split(',')[11]) != '')
if data.isEmpty():
result = sc.parallelize("0")
result.saveAsTextFile("output/media_cp_gasolina_95.txt")
else:
precio = data.map(lambda line: (int(line.split(',')[6]),float(line.split(',')[11]))) #map (CP, precio_gasolina_95)
count = data.map(lambda line: (int(line.split(',')[6]), 1)) #map (CP, contador)
aggreg1 = precio.reduceByKey(lambda a, b: a+b)
aggreg2 = count.reduceByKey(lambda a, b: a+b)
union = aggreg1.join(aggreg2)
result = union.map(lambda line: (line[1][0]/line[1][1]))
result.saveAsTextFile("output/media_cp_gasolina_95.txt")
# precio_gasoleo_a = [12]
data = text.filter(lambda line: str(line.split(',')[12]) != '')
if data.isEmpty():
result = sc.parallelize("0")
result.saveAsTextFile("output/media_cp_gasoleo_a.txt")
else:
precio = data.map(lambda line: (int(line.split(',')[6]),float(line.split(',')[12]))) #map (CP, precio_gasoleo_a)
count = data.map(lambda line: (int(line.split(',')[6]), 1)) #map (CP, contador)
aggreg1 = precio.reduceByKey(lambda a, b: a+b)
aggreg2 = count.reduceByKey(lambda a, b: a+b)
union = aggreg1.join(aggreg2)
result = union.map(lambda line: (line[1][0]/line[1][1]))
result.saveAsTextFile("output/media_cp_gasoleo_a.txt")
# precio_gasoleo_b = [13]
data = text.filter(lambda line: str(line.split(',')[13]) != '')
if data.isEmpty():
result = sc.parallelize("0")
result.saveAsTextFile("output/media_cp_gasoleo_b.txt")
else:
precio = data.map(lambda line: (int(line.split(',')[6]),float(line.split(',')[13]))) #map (CP, precio_gasoleo_b)
count = data.map(lambda line: (int(line.split(',')[6]), 1)) #map (CP, contador)
aggreg1 = precio.reduceByKey(lambda a, b: a+b)
aggreg2 = count.reduceByKey(lambda a, b: a+b)
union = aggreg1.join(aggreg2)
result = union.map(lambda line: (line[1][0]/line[1][1]))
result.saveAsTextFile("output/media_cp_gasoleo_b.txt")
# precio_bioetanol = [14]
data = text.filter(lambda line: str(line.split(',')[14]) != '')
if data.isEmpty():
result = sc.parallelize("0")
result.saveAsTextFile("output/media_cp_bioetanol.txt")
else:
precio = data.map(lambda line: (int(line.split(',')[6]),float(line.split(',')[14]))) #map (CP, precio_bioetanol)
count = data.map(lambda line: (int(line.split(',')[6]), 1)) #map (CP, contador)
aggreg1 = precio.reduceByKey(lambda a, b: a+b)
aggreg2 = count.reduceByKey(lambda a, b: a+b)
union = aggreg1.join(aggreg2)
result = union.map(lambda line: (line[1][0]/line[1][1]))
result.saveAsTextFile("output/media_cp_bioetanol.txt")
# precio_nuevo_gasoleo_a = [15]
data = text.filter(lambda line: str(line.split(',')[15]) != '')
if data.isEmpty():
result = sc.parallelize("0")
result.saveAsTextFile("output/media_cp_nuevo_gasoleo_a.txt")
else:
precio = data.map(lambda line: (int(line.split(',')[6]),float(line.split(',')[15]))) #map (CP, precio_nuevo_gasoleo_a)
count = data.map(lambda line: (int(line.split(',')[6]), 1)) #map (CP, contador)
aggreg1 = precio.reduceByKey(lambda a, b: a+b)
aggreg2 = count.reduceByKey(lambda a, b: a+b)
union = aggreg1.join(aggreg2)
result = union.map(lambda line: (line[1][0]/line[1][1]))
result.saveAsTextFile("output/media_cp_nuevo_gasoleo_a.txt")
# precio_biodiesel = [16]
data = text.filter(lambda line: str(line.split(',')[16]) != '')
if data.isEmpty():
result = sc.parallelize("0")
result.saveAsTextFile("output/media_cp_biodiesel.txt")
else:
precio = data.map(lambda line: (int(line.split(',')[6]),float(line.split(',')[16]))) #map (CP, precio_biodiesel)
count = data.map(lambda line: (int(line.split(',')[6]), 1)) #map (CP, contador)
aggreg1 = precio.reduceByKey(lambda a, b: a+b)
aggreg2 = count.reduceByKey(lambda a, b: a+b)
union = aggreg1.join(aggreg2)
result = union.map(lambda line: (line[1][0]/line[1][1]))
result.saveAsTextFile("output/media_cp_biodiesel.txt")
# f__ester_metilico = [17]
data = text.filter(lambda line: str(line.split(',')[17]) != '')
if data.isEmpty():
result = sc.parallelize("0")
result.saveAsTextFile("output/media_cp_eter_metilico.txt")
else:
precio = data.map(lambda line: (int(line.split(',')[6]),float(line.split(',')[17]))) #map (CP, precio_ester_metilico)
count = data.map(lambda line: (int(line.split(',')[6]), 1)) #map (CP, contador)
aggreg1 = precio.reduceByKey(lambda a, b: a+b)
aggreg2 = count.reduceByKey(lambda a, b: a+b)
union = aggreg1.join(aggreg2)
result = union.map(lambda line: (line[1][0]/line[1][1]))
result.saveAsTextFile("output/media_cp_ester_metilico.txt")
# f__bioalcohol = [18]
data = text.filter(lambda line: str(line.split(',')[18]) != '')
if data.isEmpty():
result = sc.parallelize("0")
result.saveAsTextFile("output/media_cp_bioalcohol.txt")
else:
precio = data.map(lambda line: (int(line.split(',')[6]),float(line.split(',')[18]))) #map (CP, bioalcohol)
count = data.map(lambda line: (int(line.split(',')[6]), 1)) #map (CP, contador)
aggreg1 = precio.reduceByKey(lambda a, b: a+b)
aggreg2 = count.reduceByKey(lambda a, b: a+b)
union = aggreg1.join(aggreg2)
result = union.map(lambda line: (line[1][0]/line[1][1]))
result.saveAsTextFile("output/media_cp_bioalcohol.txt")
# precio_gasolina_98 = [19]
data = text.filter(lambda line: str(line.split(',')[19]) != '')
if data.isEmpty():
result = sc.parallelize("0")
result.saveAsTextFile("output/media_cp_gasolina_98.txt")
else:
precio = data.map(lambda line: (int(line.split(',')[6]),float(line.split(',')[19]))) #map (CP, precio_gasolina_98)
count = data.map(lambda line: (int(line.split(',')[6]), 1)) #map (CP, contador)
aggreg1 = precio.reduceByKey(lambda a, b: a+b)
aggreg2 = count.reduceByKey(lambda a, b: a+b)
union = aggreg1.join(aggreg2)
result = union.map(lambda line: (line[1][0]/line[1][1]))
result.saveAsTextFile("output/media_cp_gasolina_98.txt")
# precio_gas_natural_comprimido = [20]
data = text.filter(lambda line: str(line.split(',')[20]) != '')
if data.isEmpty():
result = sc.parallelize("0")
result.saveAsTextFile("output/media_cp_gas_natural_comprimido.txt")
else:
precio = data.map(lambda line: (int(line.split(',')[6]),float(line.split(',')[20]))) #map (CP, precio_gas_natural_comprimido)
count = data.map(lambda line: (int(line.split(',')[6]), 1)) #map (CP, contador)
aggreg1 = precio.reduceByKey(lambda a, b: a+b)
aggreg2 = count.reduceByKey(lambda a, b: a+b)
union = aggreg1.join(aggreg2)
result = union.map(lambda line: (line[1][0]/line[1][1]))
result.saveAsTextFile("output/media_cp_gas_natural_comprimido.txt")
# precio_gas_natural_licuado = [21]
data = text.filter(lambda line: str(line.split(',')[21]) != '')
if data.isEmpty():
result = sc.parallelize("0")
result.saveAsTextFile("output/media_cp_gas_natural_licuado.txt")
else:
precio = data.map(lambda line: (int(line.split(',')[6]),float(line.split(',')[21]))) #map (CP, precio_gas_natural_licuado)
count = data.map(lambda line: (int(line.split(',')[6]), 1)) #map (CP, contador)
aggreg1 = precio.reduceByKey(lambda a, b: a+b)
aggreg2 = count.reduceByKey(lambda a, b: a+b)
union = aggreg1.join(aggreg2)
result = union.map(lambda line: (line[1][0]/line[1][1]))
result.saveAsTextFile("output/media_cp_gas_natural_licuado.txt")
# precio_gases_licuados_del_petr = [22]
data = text.filter(lambda line: str(line.split(',')[22]) != '')
if data.isEmpty():
result = sc.parallelize("0")
result.saveAsTextFile("output/media_cp_gases_licuados_del_petr.txt")
else:
precio = data.map(lambda line: (int(line.split(',')[6]),float(line.split(',')[22]))) #map (CP, precio_gases_licuados_del_petr)
count = data.map(lambda line: (int(line.split(',')[6]), 1)) #map (CP, contador)
aggreg1 = precio.reduceByKey(lambda a, b: a+b)
aggreg2 = count.reduceByKey(lambda a, b: a+b)
union = aggreg1.join(aggreg2)
result = union.map(lambda line: (line[1][0]/line[1][1]))
result.saveAsTextFile("output/media_cp_gases_licuados_del_petr.txt")
|
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] |
core/data/collates/collate_functions.py
|
cjy97/LibFewShot
| 471 |
75806
|
# -*- coding: utf-8 -*-
import itertools
from collections import Iterable
import torch
class GeneralCollateFunction(object):
"""A Generic `Collate_fn`.
For finetuning-train.
"""
def __init__(self, trfms, times):
"""Initialize a `GeneralCollateFunction`.
Args:
trfms (list): A list of torchvision transforms.
times (int): Specify the augment times. (0 or 1 for not to augment)
"""
super(GeneralCollateFunction, self).__init__()
self.trfms = trfms
self.times = times
def method(self, batch):
"""Apply transforms and augmentations on a batch.
The images and targets in a batch are augmented by the number of `self.times` and the targets are augmented
to match the shape of images.
Args:
batch (list of tuple): A batch returned by dataset.
Returns:
tuple: A tuple of (images, targets), here len(images)=len(targets).
"""
try:
images, targets = zip(*batch)
images = list(itertools.chain.from_iterable([[image] * self.times for image in images]))
images = [self.trfms(image).unsqueeze(0) for image in images]
targets = list(
itertools.chain.from_iterable([[target] * self.times for target in targets])
)
targets = [torch.tensor([target]) for target in targets]
assert len(images) == len(targets), "Inconsistent number of images and labels!"
images = torch.cat(images)
targets = torch.tensor(targets, dtype=torch.int64)
return images, targets
except TypeError:
raise TypeError(
"Error, probably because the transforms are passed to the dataset, the transforms should be "
"passed to the collate_fn"
)
def __call__(self, batch):
return self.method(batch)
class FewShotAugCollateFunction(object):
"""`Collate_fn` for few-shot dataloader.
For finetuning-val, finetuning-test and meta/metric-train/val/test.
"""
def __init__(self, trfms, times, times_q, way_num, shot_num, query_num, episode_size):
"""Initialize a `FewShotAugCollateFunction`.
Args:
trfms (list or tuple of list): A torchvision transfrom list of a tuple of 2 torchvision transform list.
if `list`, both support and query images will be applied the same transforms, otherwise the 1st one will
apply to support images and the 2nd one will apply to query images.
times (int): Augment times of support iamges
times_q (int ): Augment times of query images
way_num (int): Few-shot way setting
shot_num (int): Few-shot shot setting
query_num (int): Few-shot query setting
episode_size (int): Few-shot episode size setting
"""
super(FewShotAugCollateFunction, self).__init__()
try:
self.trfms_support, self.trfms_query = trfms
except Exception:
self.trfms_support = self.trfms_query = trfms
# Allow different trfms: when single T, apply to S and Q equally;
# When trfms=(T,T), apply to S and Q separately;
self.times = 1 if times == 0 else times
self.times_q = 1 if times_q == 0 else times_q
self.way_num = way_num
self.shot_num = shot_num
self.query_num = query_num
self.shot_aug = self.shot_num * self.times
self.query_aug = self.query_num * self.times_q
self.episode_size = episode_size
def method(self, batch):
"""Apply transforms and augmentations on a **few-shot** batch.
The samples of query and support are augmented separately.
For example: if aug_times=5, then 01234 -> 0000011111222223333344444.
Args:
batch (list of tuple): A batch returned by a few-shot dataset.
Returns:
tuple: a tuple of (images, gt_labels).
"""
try:
images, labels = zip(
*batch
) # images = [img_label_tuple[0] for img_label_tuple in batch] # 111111222222 (5s1q for example)
images_split_by_label = [
images[index : index + self.shot_num + self.query_num]
for index in range(0, len(images), self.shot_num + self.query_num)
] # 111111; 222222 ;
images_split_by_label_type = [
[spt_qry[: self.shot_num], spt_qry[self.shot_num :]]
for spt_qry in images_split_by_label
] # 11111,1;22222,2; == [shot, query]
# aug support
# fixme: should have a elegant method
# 1111111111,1;2222222222,2 (aug_time = 2 for example)
for cls in images_split_by_label_type:
cls[0] = cls[0] * self.times # aug support
cls[1] = cls[1] * self.times_q # aug query
# flatten and apply trfms
flat = lambda t: [x for sub in t for x in flat(sub)] if isinstance(t, Iterable) else [t]
images = flat(images_split_by_label_type)
# 1111111111122222222222
# images = [self.trfms(image) for image in images] # list of tensors([c, h, w])
images = [
self.trfms_support(image)
if index % (self.shot_aug + self.query_aug) < self.shot_aug
else self.trfms_query(image)
for index, image in enumerate(images)
] # list of tensors([c, h, w])
images = torch.stack(images) # [b', c, h, w] <- b' = b after aug
# labels
# global_labels = torch.tensor(labels,dtype=torch.int64)
# global_labels = torch.tensor(labels,dtype=torch.int64).reshape(self.episode_size,self.way_num,
# self.shot_num*self.times+self.query_num)
global_labels = torch.tensor(labels, dtype=torch.int64).reshape(
self.episode_size, self.way_num, self.shot_num + self.query_num
)
global_labels = (
global_labels[..., 0]
.unsqueeze(-1)
.repeat(
1,
1,
self.shot_num * self.times + self.query_num * self.times_q,
)
)
return images, global_labels
# images.shape = [e*w*(q+s) x c x h x w], global_labels.shape = [e x w x (q+s)]
except TypeError:
raise TypeError(
"Error, probably because the transforms are passed to the dataset, the transforms should be "
"passed to the collate_fn"
)
def __call__(self, batch):
return self.method(batch)
|
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13,
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1125,
13,
4706,
736,
1583,
29889,
5696,
29898,
16175,
29897,
13,
2
] |
blog/context_processors/main_menu.py
|
evg-dev/pyblog
| 0 |
162756
|
from django.core.context_processors import request
from blog.models import Category
def menu(request):
return {"category_menu": Category.objects.all(), }
|
[
1,
515,
9557,
29889,
3221,
29889,
4703,
29918,
5014,
943,
1053,
2009,
13,
3166,
12618,
29889,
9794,
1053,
17943,
13,
13,
13,
1753,
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13,
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736,
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7320,
29918,
6510,
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29889,
12650,
29889,
497,
3285,
500,
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2
] |
tests/test_file_service/test_files_grouping_by_category.py
|
Masynchin/filogram
| 0 |
159409
|
from filogram import file_service
def test_correct_files_grouping_by_category(create_unique_file):
files = []
for category in ["books", "audio", "texts", "audio"]:
file = create_unique_file(category=category)
files.append(file)
(book_file, audio_file, text_file, another_audio_file) = files
files = sort_files_by_category(files)
grouped_files = file_service.group_files_by_category(files)
assert grouped_files == {
"books": (book_file,),
"texts": (text_file,),
"audio": (audio_file, another_audio_file),
}
def sort_files_by_category(files):
"""Сортировка файлов по категории и уникальному ID.
В основном коде сортировка файлов происходит при запросе к БД.
Там они сортируются по категории, и если они совпадают, то по
полю `unique_id`.
Присвоить файлу во время создания поле `unique_id` нельзя, так как
`FileModel` является дочерним классом NamedTuple. В данной функции
`enumerate` используется в качестве замены поля `unique_id`
"""
files_sorted_by_category = sorted(
enumerate(files), key=lambda pair: (pair[1].category, pair[0])
)
files_sorted_by_category = [file for (_, file) in files_sorted_by_category]
return files_sorted_by_category
def test_correct_owned_files_grouping_by_category(
default_user, create_unique_file
):
files = []
for category in ["books", "audio", "texts", "audio"]:
file = create_unique_file(user=default_user, category=category)
file_service.save_file(file)
files.append(file)
(book_file, audio_file, text_file, another_audio_file) = files
owned_files = file_service.get_owned_files(default_user.id)
grouped_files = file_service.group_files_by_category(owned_files)
assert grouped_files == {
"books": (book_file,),
"texts": (text_file,),
"audio": (audio_file, another_audio_file),
}
|
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29918,
1445,
511,
13,
1678,
500,
13,
2
] |
Stackless/demo/fakechannel.py
|
masamitsu-murase/stackless
| 854 |
155836
|
import stackless
class MyChannel:
def __init__(self):
self.queue = []
self.balance = 0
self.temp = None
def send(self, data):
if self.balance < 0:
receiver = self.queue.pop(0)
self.temp = data
receiver.insert()
self.balance += 1
receiver.run()
else:
sender = stackless.current
self.queue.append((sender, data))
self.balance += 1
stackless.schedule_remove()
def receive(self):
if self.balance > 0:
sender, retval = self.queue.pop(0)
sender.insert()
self.balance -= 1
return retval
else:
receiver = stackless.current
self.queue.append(receiver)
self.balance -= 1
stackless.schedule_remove()
return self.temp
def f1(ch):
for i in range(5):
ch.send(i)
print("done sending")
def f2(ch):
while 1:
data = ch.receive()
if data is None:
print("done receiving")
return
print("received", data)
def test():
ch = MyChannel()
t2 = stackless.tasklet(f2)(ch)
t1 = stackless.tasklet(f1)(ch)
stackless.run()
return ch
if __name__ == "__main__":
test()
|
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1583,
29889,
5521,
749,
22361,
29871,
29896,
13,
9651,
736,
3240,
791,
13,
4706,
1683,
29901,
13,
9651,
19870,
353,
5096,
2222,
29889,
3784,
13,
9651,
1583,
29889,
9990,
29889,
4397,
29898,
13556,
2147,
29897,
13,
9651,
1583,
29889,
5521,
749,
22361,
29871,
29896,
13,
9651,
5096,
2222,
29889,
816,
11272,
29918,
5992,
580,
13,
9651,
736,
1583,
29889,
7382,
13,
13,
13,
1753,
285,
29896,
29898,
305,
1125,
13,
1678,
363,
474,
297,
3464,
29898,
29945,
1125,
13,
4706,
521,
29889,
6717,
29898,
29875,
29897,
13,
1678,
1596,
703,
15091,
9348,
1159,
13,
13,
13,
1753,
285,
29906,
29898,
305,
1125,
13,
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1550,
29871,
29896,
29901,
13,
4706,
848,
353,
521,
29889,
13556,
573,
580,
13,
4706,
565,
848,
338,
6213,
29901,
13,
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1596,
703,
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13442,
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13,
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736,
13,
4706,
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2347,
613,
848,
29897,
13,
13,
13,
1753,
1243,
7295,
13,
1678,
521,
353,
1619,
13599,
580,
13,
1678,
260,
29906,
353,
5096,
2222,
29889,
7662,
1026,
29898,
29888,
29906,
5033,
305,
29897,
13,
1678,
260,
29896,
353,
5096,
2222,
29889,
7662,
1026,
29898,
29888,
29896,
5033,
305,
29897,
13,
1678,
5096,
2222,
29889,
3389,
580,
13,
1678,
736,
521,
13,
13,
361,
4770,
978,
1649,
1275,
376,
1649,
3396,
1649,
1115,
13,
1678,
1243,
580,
13,
2
] |
ex065.py
|
raquelEllem/exerciciosPython
| 0 |
151576
|
<filename>ex065.py<gh_stars>0
cont = soma = media = maior = menor = 0
res = 'S'
while res == 'S':
num = int(input('Digite um número: '))
res = str(input('Quer continuar? [S/N] ').strip().upper())
soma += num
cont += 1
if cont == 1:
maior = menor = num
else:
if maior < num:
maior = num
if menor > num:
menor = num
media = soma / cont
print('Você digitou {} números e a média foi de {:.2f}'.format(cont, media))
print('O maior valor foi de {} e o menor foi {}'.format(maior, menor))
|
[
1,
529,
9507,
29958,
735,
29900,
29953,
29945,
29889,
2272,
29966,
12443,
29918,
303,
1503,
29958,
29900,
13,
1285,
353,
1047,
29874,
353,
5745,
353,
17136,
353,
26764,
353,
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29900,
13,
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353,
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640,
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13,
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29901,
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353,
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353,
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13,
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565,
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529,
954,
29901,
13,
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17136,
353,
954,
13,
4706,
565,
26764,
1405,
954,
29901,
13,
9651,
26764,
353,
954,
13,
9799,
353,
1047,
29874,
847,
640,
13,
2158,
877,
29963,
542,
30037,
13615,
283,
6571,
12158,
359,
321,
263,
10283,
423,
4732,
316,
12365,
29889,
29906,
29888,
29913,
4286,
4830,
29898,
1285,
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5745,
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13,
2158,
877,
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17136,
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4732,
316,
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321,
288,
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6571,
4286,
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1611,
29892,
26764,
876,
13,
2
] |
warprnnt_numba/rnnt_loss/rnnt_atomic_locks/utils/naive_gpu_rnnt_kernel.py
|
titu1994/warprnnt_numba
| 7 |
126693
|
# Copyright (c) 2021, NVIDIA CORPORATION. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
#
# Copyright 2018-2019, <NAME>
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
import math
import torch
from numba import cuda
from warprnnt_numba.rnnt_loss.utils import rnnt_helper
from warprnnt_numba.rnnt_loss.utils.cuda_utils.gpu_rnnt_kernel import logp
GPU_RNNT_THREAD_SIZE = 256
@cuda.jit(device=True, inline=True)
def logp(
denom: torch.Tensor, acts: torch.Tensor, maxT: int, maxU: int, alphabet_size: int, mb: int, t: int, u: int, v: int
):
"""
Compute the sum of log probability from the activation tensor and its denominator.
Args:
denom: Tensor of shape [B, T, U] flattened. Represents the denominator of the logprobs activation tensor
across entire vocabulary.
acts: Tensor of shape [B, T, U, V+1] flattened. Represents the logprobs activation tensor.
maxT: The maximum possible acoustic sequence length. Represents T in the logprobs tensor.
maxU: The maximum possible target sequence length. Represents U in the logprobs tensor.
alphabet_size: The vocabulary dimension V+1 (inclusive of RNNT blank).
mb: Batch indexer.
t: Acoustic sequence timestep indexer.
u: Target sequence timestep indexer.
v: Vocabulary token indexer.
Returns:
The sum of logprobs[mb, t, u, v] + denom[mb, t, u]
"""
col = (mb * maxT + t) * maxU + u
return denom[col] + acts[col * alphabet_size + v]
@cuda.jit()
def compute_alphas_kernel_atomic_locks(
acts: torch.Tensor,
denom: torch.Tensor,
alphas: torch.Tensor,
llForward: torch.Tensor,
xlen: torch.Tensor,
ylen: torch.Tensor,
mlabels: torch.Tensor, # [B]
minibatch: int,
maxT: int,
maxU: int,
alphabet_size: int,
blank: int,
lock: torch.Tensor,
):
"""
Compute alpha (forward variable) probabilities over the transduction step in loop,
with CUDA atomic locks.
Baseline reference from SpeechBrain implementation -
https://github.com/speechbrain/speechbrain/blob/develop/speechbrain/nnet/loss/transducer_loss.py
Args:
acts: Tensor of shape [B, T, U, V+1]. Represents the logprobs activation tensor.
denom: Tensor of shape [B, T, U] flattened. Represents the denominator of the logprobs activation tensor
across entire vocabulary.
alphas: Zero tensor of shape [B, T, U]. Will be updated inside the kernel with the forward variable
probabilities.
llForward: Zero tensor of shape [B]. Represents the log-likelihood of the forward pass.
Returned as the forward pass loss that is reduced by the optimizer.
xlen: Vector of length B which contains the actual acoustic sequence lengths in the padded
activation tensor.
ylen: Vector of length B which contains the actual target sequence lengths in the padded
activation tensor.
mlabels: Matrix of shape [B, U+1] (+1 here is due to <SOS> token - usually the RNNT blank).
The matrix contains the padded target transcription that must be predicted.
minibatch: Int representing the batch size.
maxT: The maximum possible acoustic sequence length. Represents T in the logprobs tensor.
maxU: The maximum possible target sequence length. Represents U in the logprobs tensor.
alphabet_size: The vocabulary dimension V+1 (inclusive of RNNT blank).
blank_: Index of the RNNT blank token in the vocabulary. Generally the first or last token in the vocab.
lock: Tensor of shape [B, U+1]. Bool values, used for CUDA atomic locking.
Updates:
Kernel inplace updates the following inputs:
- alphas: forward variable scores.
- llForward: log-likelihood of forward variable.
"""
# // launch B blocks, each block has U threads
b = cuda.blockIdx.x
u = cuda.threadIdx.x
T = xlen[b] # select AM length of current sample
U = ylen[b] + 1 # select target length of current sample, +1 for the blank token
labels: torch.Tensor = mlabels[b] # mb label start point, equivalent to mlabels + b * (maxU - 1)
offset = b * maxT * maxU # pointer indexing offset
t = 0
# Ordinary alpha calculations, broadcast across B=b and U=u
# Look up forward variable calculation from rnnt_numpy.forward_pass()
if u < U:
# for each (B,U) Thread
# wait the unlock of the previous computation of Alpha[b,U-1,:]
# Do the computation over the whole Time sequence on alpha[B,U,:]
# and then unlock the target U+1 for computation
while t < T:
if u == 0:
# for t in range(1, T) step to initialize alphas[b, t, 0]
if t > 0:
alphas[offset + t * maxU] = (
alphas[offset + (t - 1) * maxU]
+ logp(denom, acts, maxT, maxU, alphabet_size, b, t - 1, 0, blank)
)
cuda.atomic.add(lock, (b, u + 1), -1)
t += 1
else:
if cuda.atomic.add(lock, (b, u), 0) < 0:
# for u in range(1, U) step to initialize alphas[b, 0, u]
if t == 0:
alphas[offset + u] = (
alphas[offset + u - 1]
+ logp(denom, acts, maxT, maxU, alphabet_size, b, 0, u - 1, labels[u - 1])
)
else:
# for t in range(1, T) for u in range(1, U) step to compute alphas[b, t, u]
emit = (
alphas[offset + t * maxU + u - 1]
+ logp(denom, acts, maxT, maxU, alphabet_size, b, t, u - 1, labels[u - 1])
)
no_emit = (
alphas[offset + (t - 1) * maxU + u]
+ logp(denom, acts, maxT, maxU, alphabet_size, b, t - 1, u, blank)
)
alphas[offset + t * maxU + u] = max(no_emit, emit) + math.log1p(
math.exp(-abs(no_emit - emit))
)
if u < U:
cuda.atomic.add(lock, (b, u + 1), -1)
cuda.atomic.add(lock, (b, u), 1)
t += 1
# After final sync, alphas[b, T-1, U - 1] + logprobs[b, T-1, U-1, blank] + denom[b, T-1, U-1] gives
# log-likelihood of forward pass.
if u == U - 1:
# for each thread b (utterance)
llForward[b] = (
alphas[offset + (T - 1) * maxU + U - 1]
+ logp(denom, acts, maxT, maxU, alphabet_size, b, T - 1, U - 1, blank)
)
@cuda.jit()
def compute_betas_kernel_atomic_locks(
acts: torch.Tensor,
denom: torch.Tensor,
betas: torch.Tensor,
llBackward: torch.Tensor,
xlen: torch.Tensor,
ylen: torch.Tensor,
mlabels: torch.Tensor, # [B, U]
minibatch: int,
maxT: int,
maxU: int,
alphabet_size: int,
blank_: int,
lock: torch.Tensor,
):
"""
Compute beta (backward variable) probabilities over the transduction step.
Args:
acts: Tensor of shape [B, T, U, V+1] flattened. Represents the logprobs activation tensor.
denom: Tensor of shape [B, T, U] flattened. Represents the denominator of the logprobs activation tensor
across entire vocabulary.
betas: Zero tensor of shape [B, T, U]. Will be updated inside the kernel with the backward variable
probabilities.
llBackward: Zero tensor of shape [B]. Represents the log-likelihood of the backward pass.
Returned as the backward pass loss that is reduced by the optimizer.
xlen: Vector of length B which contains the actual acoustic sequence lengths in the padded
activation tensor.
ylen: Vector of length B which contains the actual target sequence lengths in the padded
activation tensor.
mlabels: Matrix of shape [B, U+1] (+1 here is due to <SOS> token - usually the RNNT blank).
The matrix contains the padded target transcription that must be predicted.
minibatch: Int representing the batch size.
maxT: The maximum possible acoustic sequence length. Represents T in the logprobs tensor.
maxU: The maximum possible target sequence length. Represents U in the logprobs tensor.
alphabet_size: The vocabulary dimension V+1 (inclusive of RNNT blank).
blank_: Index of the RNNT blank token in the vocabulary. Generally the first or last token in the vocab.
lock: Tensor of shape [B, U+1]. Bool values, used for CUDA atomic locking.
Updates:
Kernel inplace updates the following inputs:
- betas: backward variable scores.
- llBackward: log-likelihood of backward variable.
"""
# // launch B blocks, each block has U threads
b = cuda.blockIdx.x # // batch id
u = cuda.threadIdx.x # label id, u
T = xlen[b] # select AM length of current sample
U = ylen[b] + 1 # select target length of current sample, +1 for the blank token
offset = b * maxT * maxU # pointer indexing offset
labels: torch.Tensor = mlabels[b] # mb label start point, equivalent to mlabels + b * (maxU - 1)
# Initilize beta[b, t=T-1, u=U-1] for all b in B with log_probs[b, t=T-1, u=U-1, blank]
t = T - 1
# Ordinary beta calculations, broadcast across B=b and U=u
# Look up backward variable calculation from rnnt_numpy.backward_pass()
if u < U:
# Loop in reversed order of T - (reversed(range(T - 1)) steps
while t >= 0:
if u == U - 1:
# Initilize beta[b, t=T-1, u=U-1] for all b in B with log_probs[b, t=T-1, u=U-1, blank]
if t == T - 1:
betas[offset + t * maxU + u] = logp(denom, acts, maxT, maxU, alphabet_size, b, t, u, blank_)
else:
# for t in reversed(range(T - 1)) step to initialize betas[b, t, U-1]
betas[offset + t * maxU + u] = (
betas[offset + (t + 1) * maxU + u]
+ logp(denom, acts, maxT, maxU, alphabet_size, b, t, u, blank_)
)
cuda.atomic.add(lock, (b, u - 1), -1)
t -= 1
else:
if cuda.atomic.add(lock, (b, u), 0) < 0:
if t == T - 1:
# Compute betas[b, T - 1, u]
betas[offset + t * maxU + u] = (
betas[offset + t * maxU + u + 1]
+ logp(denom, acts, maxT, maxU, alphabet_size, b, t, u, labels[u])
)
else:
# for t in reversed(range(T - 1)) for u in reversed(range(U - 1)) step to compute betas[b, t, u]
emit = (
betas[offset + t * maxU + u + 1]
+ logp(denom, acts, maxT, maxU, alphabet_size, b, t, u, labels[u])
)
no_emit = (betas[offset + (t + 1) * maxU + u]
+ logp(denom, acts, maxT, maxU, alphabet_size, b, t, u, blank_))
betas[offset + t * maxU + u] = rnnt_helper.log_sum_exp(emit, no_emit)
if u > 0:
cuda.atomic.add(lock, (b, u - 1), -1)
cuda.atomic.add(lock, (b, u), 1)
t -= 1
# After final sync, betas[b, 0, 0] gives
# log-likelihood of backward pass.
if u == 0:
llBackward[b] = betas[offset]
|
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] |
devito/ir/iet/efunc.py
|
fffarias/devito-1
| 199 |
1611713
|
<filename>devito/ir/iet/efunc.py
from collections import namedtuple
from cached_property import cached_property
from sympy import Or
import cgen as c
import numpy as np
from devito.ir.iet.nodes import (BlankLine, Call, Callable, Conditional, Dereference,
DummyExpr, Iteration, List, PointerCast, Return, While,
CallableBody)
from devito.ir.iet.utils import derive_parameters, diff_parameters
from devito.symbolics import CondEq, CondNe, FieldFromComposite, FieldFromPointer, Macro
from devito.tools import as_tuple
from devito.types import PThreadArray, SharedData, Symbol, Pointer
__all__ = ['ElementalFunction', 'ElementalCall', 'make_efunc',
'EntryFunction', 'ThreadFunction', 'make_thread_ctx',
'DeviceFunction']
# ElementalFunction machinery
class ElementalFunction(Callable):
"""
A Callable performing a computation over an abstract convex iteration space.
A Call to an ElementalFunction will "instantiate" such iteration space by
supplying bounds and step increment for each Dimension listed in
``dynamic_parameters``.
"""
is_ElementalFunction = True
def __init__(self, name, body, retval, parameters=None, prefix=('static', 'inline'),
dynamic_parameters=None):
super(ElementalFunction, self).__init__(name, body, retval, parameters, prefix)
self._mapper = {}
for i in as_tuple(dynamic_parameters):
if i.is_Dimension:
self._mapper[i] = (parameters.index(i.symbolic_min),
parameters.index(i.symbolic_max))
else:
self._mapper[i] = (parameters.index(i),)
@cached_property
def dynamic_defaults(self):
return {k: tuple(self.parameters[i] for i in v) for k, v in self._mapper.items()}
def make_call(self, dynamic_args_mapper=None, incr=False, retobj=None,
is_indirect=False):
return ElementalCall(self.name, list(self.parameters), dict(self._mapper),
dynamic_args_mapper, incr, retobj, is_indirect)
class ElementalCall(Call):
def __init__(self, name, arguments=None, mapper=None, dynamic_args_mapper=None,
incr=False, retobj=None, is_indirect=False):
self._mapper = mapper or {}
arguments = list(as_tuple(arguments))
dynamic_args_mapper = dynamic_args_mapper or {}
for k, v in dynamic_args_mapper.items():
tv = as_tuple(v)
# Sanity check
if k not in self._mapper:
raise ValueError("`k` is not a dynamic parameter" % k)
if len(self._mapper[k]) != len(tv):
raise ValueError("Expected %d values for dynamic parameter `%s`, given %d"
% (len(self._mapper[k]), k, len(tv)))
# Create the argument list
for i, j in zip(self._mapper[k], tv):
arguments[i] = j if incr is False else (arguments[i] + j)
super(ElementalCall, self).__init__(name, arguments, retobj, is_indirect)
def _rebuild(self, *args, dynamic_args_mapper=None, incr=False,
retobj=None, **kwargs):
# This guarantees that `ec._rebuild(arguments=ec.arguments) == ec`
return super(ElementalCall, self)._rebuild(
*args, dynamic_args_mapper=dynamic_args_mapper, incr=incr,
retobj=retobj, **kwargs
)
@cached_property
def dynamic_defaults(self):
return {k: tuple(self.arguments[i] for i in v) for k, v in self._mapper.items()}
def make_efunc(name, iet, dynamic_parameters=None, retval='void', prefix='static'):
"""
Shortcut to create an ElementalFunction.
"""
return ElementalFunction(name, iet, retval, derive_parameters(iet), prefix,
dynamic_parameters)
# EntryFunction machinery
class EntryFunction(Callable):
pass
# ThreadFunction machinery
ThreadCtx = namedtuple('ThreadCtx', 'threads sdata funcs init activate finalize')
class ThreadFunction(Callable):
"""
A Callable executed asynchronously by a separate thread.
"""
pass
def _make_threads(value, sregistry):
name = sregistry.make_name(prefix='threads')
base_id = 1 + sum(i.size for i in sregistry.npthreads)
if value is None:
# The npthreads Symbol isn't actually used, but we record the fact
# that some new pthreads have been allocated
sregistry.make_npthreads(1)
npthreads = 1
else:
npthreads = sregistry.make_npthreads(value)
threads = PThreadArray(name=name, npthreads=npthreads, base_id=base_id)
return threads
def _make_thread_init(threads, tfunc, isdata, sdata, sregistry):
d = threads.index
if threads.size == 1:
callback = lambda body: body
else:
callback = lambda body: Iteration(body, d, threads.size - 1)
# A unique identifier for each created pthread
pthreadid = d + threads.base_id
# Initialize `sdata`
arguments = list(isdata.parameters)
arguments[-3] = sdata.symbolic_base + d
arguments[-2] = pthreadid
arguments[-1] = sregistry.deviceid
call0 = Call(isdata.name, arguments)
# Create pthreads
call1 = Call('pthread_create', (threads.symbolic_base + d,
Macro('NULL'),
Call(tfunc.name, [], is_indirect=True),
sdata.symbolic_base + d))
threadsinit = List(
header=c.Comment("Fire up and initialize `%s`" % threads.name),
body=callback([call0, call1])
)
return threadsinit
def _make_thread_func(name, iet, root, threads, sregistry):
sid = SharedData._symbolic_id
sdeviceid = SharedData._symbolic_deviceid
# Create the SharedData, that is the data structure that will be used by the
# main thread to pass information dows to the child thread(s)
required, parameters, dynamic_parameters = diff_parameters(iet, root, [sid])
parameters = sorted(parameters, key=lambda i: i.is_Function) # Allow casting
sdata = SharedData(name=sregistry.make_name(prefix='sdata'), npthreads=threads.size,
fields=required, dynamic_fields=dynamic_parameters)
# Create a Callable to initialize `sdata` with the known const values
sbase = sdata.symbolic_base
iname = 'init_%s' % sdata.dtype._type_.__name__
ibody = [DummyExpr(FieldFromPointer(i._C_name, sbase), i._C_symbol)
for i in parameters]
ibody.extend([
BlankLine,
DummyExpr(FieldFromPointer(sdata._field_id, sbase), sid),
DummyExpr(FieldFromPointer(sdata._field_deviceid, sbase), sdeviceid),
DummyExpr(FieldFromPointer(sdata._field_flag, sbase), 1)
])
iparameters = parameters + [sdata, sid, sdeviceid]
isdata = Callable(iname, ibody, 'void', iparameters, 'static')
# Prepend the SharedData fields available upon thread activation
preactions = [DummyExpr(i, FieldFromPointer(i.name, sbase))
for i in dynamic_parameters]
# Append the flag reset
postactions = [List(body=[
BlankLine,
DummyExpr(FieldFromPointer(sdata._field_flag, sbase), 1)
])]
iet = List(body=preactions + [iet] + postactions)
# The thread has work to do when it receives the signal that all locks have
# been set to 0 by the main thread
iet = Conditional(CondEq(FieldFromPointer(sdata._field_flag, sbase), 2), iet)
# The thread keeps spinning until the alive flag is set to 0 by the main thread
iet = While(CondNe(FieldFromPointer(sdata._field_flag, sbase), 0), iet)
# pthread functions expect exactly one argument, a void*, and must return void*
tretval = 'void*'
tparameter = Pointer(name='_%s' % sdata.name, dtype=np.void)
# Unpack `sdata`
unpack = [PointerCast(sdata, tparameter), BlankLine]
for i in parameters:
if i.is_AbstractFunction:
unpack.append(Dereference(i, sdata))
else:
unpack.append(DummyExpr(i, FieldFromPointer(i.name, sbase)))
unpack.append(DummyExpr(sid, FieldFromPointer(sdata._field_id, sbase)))
unpack.append(DummyExpr(sdeviceid, FieldFromPointer(sdata._field_deviceid, sbase)))
iet = CallableBody([iet, Return(Macro('NULL'))], unpacks=unpack)
tfunc = ThreadFunction(name, iet, tretval, tparameter, 'static')
return tfunc, isdata, sdata
def _make_thread_activate(threads, sdata, sync_ops, sregistry):
if threads.size == 1:
d = threads.index
else:
d = Symbol(name=sregistry.make_name(prefix=threads.index.name))
sync_locks = [s for s in sync_ops if s.is_SyncLock]
condition = Or(*([CondNe(s.handle, 2) for s in sync_locks] +
[CondNe(FieldFromComposite(sdata._field_flag, sdata[d]), 1)]))
if threads.size == 1:
activation = [While(condition)]
else:
activation = [DummyExpr(d, 0),
While(condition, DummyExpr(d, (d + 1) % threads.size))]
activation.extend([DummyExpr(FieldFromComposite(i.name, sdata[d]), i)
for i in sdata.dynamic_fields])
activation.extend([DummyExpr(s.handle, 0) for s in sync_locks])
activation.append(DummyExpr(FieldFromComposite(sdata._field_flag, sdata[d]), 2))
activation = List(
header=[c.Line(), c.Comment("Activate `%s`" % threads.name)],
body=activation,
footer=c.Line()
)
return activation
def _make_thread_finalize(threads, sdata):
d = threads.index
if threads.size == 1:
callback = lambda body: body
else:
callback = lambda body: Iteration(body, d, threads.size - 1)
threadswait = List(
header=c.Comment("Wait for completion of `%s`" % threads.name),
body=callback([
While(CondEq(FieldFromComposite(sdata._field_flag, sdata[d]), 2)),
DummyExpr(FieldFromComposite(sdata._field_flag, sdata[d]), 0),
Call('pthread_join', (threads[d], Macro('NULL')))
])
)
return threadswait
def make_thread_ctx(name, iet, root, npthreads, sync_ops, sregistry):
"""
Shortcut to create a ThreadFunction and all support data structures and
routines to implement communication between the main thread and the child
threads executing the ThreadFunction.
"""
threads = _make_threads(npthreads, sregistry)
tfunc, isdata, sdata = _make_thread_func(name, iet, root, threads, sregistry)
init = _make_thread_init(threads, tfunc, isdata, sdata, sregistry)
activate = _make_thread_activate(threads, sdata, sync_ops, sregistry)
finalize = _make_thread_finalize(threads, sdata)
return ThreadCtx(threads, sdata, [tfunc, isdata], init, activate, finalize)
# DeviceFunction machinery
class DeviceFunction(Callable):
"""
A Callable executed asynchronously on a device.
"""
def __init__(self, name, body, retval='void', parameters=None, prefix='__global__'):
super().__init__(name, body, retval, parameters=parameters, prefix=prefix)
|
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10944,
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13506,
29897,
13,
2
] |
src/opencmiss/utils/iron/bindings/python/opencmiss/iron/_utils.in.py
|
tsalemink/opencmiss.utils
| 0 |
48226
|
"""Utility routines and classes used by OpenCMISS
"""
from . import _@IRON_PYTHON_MODULE@
class CMFEError(Exception):
"""Base class for errors in the OpenCMISS library"""
pass
class CMFEType(object):
"""Base class for all OpenCMISS types"""
pass
class Enum(object):
pass
def wrap_cmiss_routine(routine, args=None):
"""Wrap a call to the OpenCMISS SWIG module
Call the routine and check the return value, and raise an
exception if it is non-zero.
Return any other remaining return values.
"""
if args is None:
r = routine()
else:
#Replace wrapped cmiss types with the underlying type
new_args = []
for arg in args:
if hasattr(arg, 'cmiss_type'):
new_args.append(arg.cmiss_type)
else:
try:
# Try to convert a list of CMISS types first.
# Check length to avoid empty strings being converted
# to an empty list
if len(arg) > 0:
new_args.append([a.cmiss_type for a in arg])
else:
new_args.append(arg)
except (TypeError, AttributeError):
new_args.append(arg)
r = routine(*new_args)
# We will either have a list of multiple return values, or
# a single status code as a return. Don't have to worry about
# ever having a single return value as a list as there will always
# be at least a return status.
if isinstance(r, list):
status = r[0]
if len(r) == 1:
return_val = None
elif len(r) == 2:
return_val = r[1]
else:
return_val = r[1:]
else:
status = r
return_val = None
if status != _@[email protected]_NO_ERROR:
if status == _@[email protected]_POINTER_IS_NULL:
raise CMFEError("CMFE type pointer is null")
elif status == _@[email protected]_POINTER_NOT_NULL:
raise CMFEError("CMFE type pointer is not null")
elif status == _@[email protected]_COULD_NOT_ALLOCATE_POINTER:
raise CMFEError("Could not allocate pointer")
elif status == _@[email protected]_ERROR_CONVERTING_POINTER:
raise CMFEError("Error converting pointer")
else:
raise CMFEError(_@[email protected]_ExtractErrorMessage()[1])
return return_val
|
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5647,
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3728,
580,
29961,
29896,
2314,
13,
1678,
736,
736,
29918,
791,
13,
2
] |
filesystems/click.py
|
Julian/Filesystems
| 2 |
37021
|
<reponame>Julian/Filesystems<gh_stars>1-10
"""
Click support for `filesystems.Path`.
"""
from __future__ import absolute_import
import click
import filesystems
class Path(click.ParamType):
name = "path"
def convert(self, value, param, context):
if not isinstance(value, str):
return value
return filesystems.Path.from_string(value)
PATH = Path()
|
[
1,
529,
276,
1112,
420,
29958,
27501,
713,
29914,
10547,
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] |
grr/server/hunts/results.py
|
nickamon/grr
| 0 |
2748
|
<gh_stars>0
#!/usr/bin/env python
"""Classes to store and manage hunt results.
"""
from grr.lib import rdfvalue
from grr.lib import registry
from grr.lib.rdfvalues import structs as rdf_structs
from grr_response_proto import jobs_pb2
from grr.server import access_control
from grr.server import aff4
from grr.server import data_store
from grr.server import sequential_collection
from grr.server.aff4_objects import aff4_queue
class HuntResultNotification(rdf_structs.RDFProtoStruct):
protobuf = jobs_pb2.HuntResultNotification
rdf_deps = [
rdfvalue.RDFDatetime,
rdfvalue.RDFURN,
]
def ResultRecord(self):
# TODO(amoser): The subpath could be part of the notification.
return data_store.Record(
queue_id=self.result_collection_urn,
timestamp=self.timestamp,
suffix=self.suffix,
subpath="Results",
value=None)
RESULT_NOTIFICATION_QUEUE = rdfvalue.RDFURN("aff4:/hunt_results_queue")
class HuntResultQueue(aff4_queue.Queue):
"""A global queue of hunt results which need to be processed."""
rdf_type = HuntResultNotification
@classmethod
def ClaimNotificationsForCollection(cls,
token=None,
start_time=None,
lease_time=200,
collection=None):
"""Return unclaimed hunt result notifications for collection.
Args:
token: The security token to perform database operations with.
start_time: If set, an RDFDateTime indicating at what point to start
claiming notifications. Only notifications with a timestamp after this
point will be claimed.
lease_time: How long to claim the notifications for.
collection: The urn of the collection to find notifications for. If unset,
the earliest (unclaimed) notification will determine the collection.
Returns:
A pair (collection, results) where collection is the collection
that notifications were retrieved for and results is a list of
Record objects which identify GrrMessage within the result
collection.
"""
class CollectionFilter(object):
def __init__(self, collection):
self.collection = collection
def FilterRecord(self, notification):
if self.collection is None:
self.collection = notification.result_collection_urn
return self.collection != notification.result_collection_urn
f = CollectionFilter(collection)
results = []
with aff4.FACTORY.OpenWithLock(
RESULT_NOTIFICATION_QUEUE,
aff4_type=HuntResultQueue,
lease_time=300,
blocking=True,
blocking_sleep_interval=15,
blocking_lock_timeout=600,
token=token) as queue:
for record in queue.ClaimRecords(
record_filter=f.FilterRecord,
start_time=start_time,
timeout=lease_time,
limit=100000):
results.append(record)
return (f.collection, results)
@classmethod
def DeleteNotifications(cls, records, token=None):
"""Delete hunt notifications."""
cls.DeleteRecords(records, token=token)
class HuntResultCollection(sequential_collection.GrrMessageCollection):
"""Sequential HuntResultCollection."""
@classmethod
def StaticAdd(cls,
collection_urn,
rdf_value,
mutation_pool=None,
timestamp=None,
suffix=None,
**kwargs):
ts = super(HuntResultCollection, cls).StaticAdd(
collection_urn,
rdf_value,
mutation_pool=mutation_pool,
timestamp=timestamp,
suffix=suffix,
**kwargs)
HuntResultQueue.StaticAdd(
RESULT_NOTIFICATION_QUEUE,
HuntResultNotification(
result_collection_urn=collection_urn, timestamp=ts[0],
suffix=ts[1]),
mutation_pool=mutation_pool)
return ts
class ResultQueueInitHook(registry.InitHook):
pre = [aff4.AFF4InitHook]
def Run(self):
try:
with aff4.FACTORY.Create(
RESULT_NOTIFICATION_QUEUE,
HuntResultQueue,
mode="w",
token=aff4.FACTORY.root_token):
pass
except access_control.UnauthorizedAccess:
pass
|
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1891,
6638,
29901,
13,
418,
1209,
13,
2
] |
selected_cols.py
|
launis/areadata
| 0 |
113614
|
def selected_cols(largeset = False, parties=True, pnroalue = True):
if largeset==True:
numeric_features = ['Miehet, 2018 (HE) osuudesta asukkaat',
'Naiset, 2018 (HE) osuudesta asukkaat',
'Asuntojen keskipinta-ala, 2018 (RA) osuus total',
'Talotyypit yhteensä 2019 Neliöhinta (EUR/m2) osuus total',
'Asumisväljyys, 2018 (TE) osuus total',
'Asukkaiden keski-ikä, 2018 (HE) osuus total',
'Postinumeroalueen pinta-ala osuus area_code',
'Perusasteen suorittaneet, 2018 (KO) osuudesta asukkaat',
'Ammatillisen tutkinnon suorittaneet, 2018 (KO) osuudesta asukkaat',
'Ylioppilastutkinnon suorittaneet, 2018 (KO) osuudesta asukkaat',
'Alemman korkeakoulututkinnon suorittaneet, 2018 (KO) osuudesta asukkaat',
'Ylemmän korkeakoulututkinnon suorittaneet, 2018 (KO) osuudesta asukkaat',
'Asukkaiden mediaanitulot, 2017 (HR) osuudesta asukkaat',
'Asukkaiden ostovoimakertymä, 2017 (HR) osuudesta asukkaat osuus total',
'Kesämökit yhteensä, 2018 (RA) osuudesta asukkaat',
'Pientaloasunnot, 2018 (RA) osuudesta asukkaat',
'Työlliset, 2017 (PT) osuudesta asukkaat',
'Työttömät, 2017 (PT) osuudesta asukkaat',
'Opiskelijat, 2017 (PT) osuudesta asukkaat',
'A Maatalous, metsätalous ja kalatalous, 2017 (TP) osuudesta asukkaat',
'B Kaivostoiminta ja louhinta, 2017 (TP) osuudesta asukkaat',
'C Teollisuus, 2017 (TP) osuudesta asukkaat',
'D Sähkö-, kaasu- ja lämpöhuolto, jäähdytysliiketoiminta, 2017 (TP) osuudesta asukkaat',
'E Vesihuolto, viemäri- ja jätevesihuolto ja muu ympäristön puhtaanapito, 2017 (TP) osuudesta asukkaat',
'F Rakentaminen, 2017 (TP) osuudesta asukkaat',
'G Tukku- ja vähittäiskauppa; moottoriajoneuvojen ja moottoripyörien korjaus, 2017 (TP) osuudesta asukkaat',
'H Kuljetus ja varastointi, 2017 (TP) osuudesta asukkaat',
'I Majoitus- ja ravitsemistoiminta, 2017 (TP) osuudesta asukkaat',
'J Informaatio ja viestintä, 2017 (TP) osuudesta asukkaat',
'K Rahoitus- ja vakuutustoiminta, 2017 (TP) osuudesta asukkaat',
'L Kiinteistöalan toiminta, 2017 (TP) osuudesta asukkaat',
'M Ammatillinen, tieteellinen ja tekninen toiminta, 2017 (TP) osuudesta asukkaat',
'N Hallinto- ja tukipalvelutoiminta, 2017 (TP) osuudesta asukkaat',
'O Julkinen hallinto ja maanpuolustus; pakollinen sosiaalivakuutus, 2017 (TP) osuudesta asukkaat',
'P Koulutus, 2017 (TP) osuudesta asukkaat',
'Q Terveys- ja sosiaalipalvelut, 2017 (TP) osuudesta asukkaat',
'R Taiteet, viihde ja virkistys, 2017 (TP) osuudesta asukkaat',
'S Muu palvelutoiminta, 2017 (TP) osuudesta asukkaat',
'T Kotitalouksien toiminta työnantajina; kotitalouksien eriyttämätön toiminta tavaroiden ja palveluiden tuottamiseksi omaan käyttöön, 2017 (TP) osuudesta asukkaat',
'U Kansainvälisten organisaatioiden ja toimielinten toiminta, 2017 (TP) osuudesta asukkaat',
'Ravintolat osuudesta asukkaat',
'Myymälät osuudesta asukkaat',
'Nuorten yksinasuvien taloudet, 2018 (TE) osuudesta taloudet',
'Lapsettomat nuorten parien taloudet, 2018 (TE) osuudesta taloudet',
'Lapsitaloudet, 2018 (TE) osuudesta taloudet',
'Teini-ikäisten lasten taloudet, 2018 (TE) osuudesta taloudet',
'Aikuisten taloudet, 2018 (TE) osuudesta taloudet',
'Eläkeläisten taloudet, 2018 (TE) osuudesta taloudet',
'Omistusasunnoissa asuvat taloudet, 2018 (TE) osuudesta taloudet',
'Vuokra-asunnoissa asuvat taloudet, 2018 (TE) osuudesta taloudet',
'Alimpaan tuloluokkaan kuuluvat taloudet, 2017 (TR) osuudesta taloudet',
'Keskimmäiseen tuloluokkaan kuuluvat taloudet, 2017 (TR) osuudesta taloudet',
'Ylimpään tuloluokkaan kuuluvat taloudet, 2017 (TR) osuudesta taloudet',
'1.1.3 Kirkollisvero Yhteensä Saajien lukumäärä osuudesta verotetut',
'11. Yrittäjätulot yhteensä Yhteensä Saajien lukumäärä osuudesta verotetut',
'4.2.10 Muut veronalaiset sosiaalietuudet Yhteensä Saajien lukumäärä osuudesta verotetut',
'4.2.6 Tapaturma- ym. muut päivärahat Yhteensä Saajien lukumäärä osuudesta verotetut',
'4.2.7 Päivä- ja äitiyspäivärahat Yhteensä Saajien lukumäärä osuudesta verotetut',
'4.2.8 Lapsen kotihoidon tuki Yhteensä Saajien lukumäärä osuudesta verotetut',
'4.2.9 Kuntoutusraha ja -avustus Yhteensä Saajien lukumäärä osuudesta verotetut',
'5.1.3 Vuokratulo Yhteensä Saajien lukumäärä osuudesta verotetut',
'11.5.1.1 Asuntolainan korkomenot (TVL 58 § 2 mom.) Yhteensä Summa, euroa osuus total',
'14.12 Opintolainavähennys Yhteensä Summa, euroa osuus total',
'14.7 Kotitalousvähennys Yhteensä Summa, euroa osuus total',
'2. Tulot yhteensä Yhteensä Summa, euroa osuus total',
'1.1 Tuloverot yhteensä Yhteensä Summa, euroa osuus total',
'4.1.C Autoetu Yhteensä Summa, euroa osuus total',
'4.2 Työmatkakulujen perusteella palkkatulosta vähennettävä määrä (TVL 93 §) Yhteensä Summa, euroa osuus total',
'5. Pääomatulot yhteensä Yhteensä Summa, euroa osuus total',
'Ruotsinkielisten osuus väestöstä, %, 2019',
'Taajama-aste, %, 2018',
'Ulkomaan kansalaisten osuus väestöstä, %, 2019',
'Kuntien välinen muuttovoitto/-tappio, henkilöä, 2019 osuudesta asukkaat nollatta',
'etaisyys osuus area_code','tkalue',
'Tuloveroprosentti']
else:
numeric_features = ['Miehet, 2018 (HE) osuudesta asukkaat',
'Asuntojen keskipinta-ala, 2018 (RA) osuus total',
'Talotyypit yhteensä 2019 Neliöhinta (EUR/m2) osuus total',
'Asumisväljyys, 2018 (TE) osuus total',
'Asukkaiden keski-ikä, 2018 (HE) osuus total',
'Postinumeroalueen pinta-ala osuus area_code',
'Perusasteen suorittaneet, 2018 (KO) osuudesta asukkaat',
'Ammatillisen tutkinnon suorittaneet, 2018 (KO) osuudesta asukkaat',
'Ylioppilastutkinnon suorittaneet, 2018 (KO) osuudesta asukkaat',
'Alemman korkeakoulututkinnon suorittaneet, 2018 (KO) osuudesta asukkaat',
'Ylemmän korkeakoulututkinnon suorittaneet, 2018 (KO) osuudesta asukkaat',
'Asukkaiden ostovoimakertymä, 2017 (HR) osuudesta asukkaat osuus total',
'Kerrostaloasunnot, 2018 (RA) osuudesta asukkaat',
'Kesämökit yhteensä, 2018 (RA) osuudesta asukkaat',
'Pientaloasunnot, 2018 (RA) osuudesta asukkaat',
'Työlliset, 2017 (PT) osuudesta asukkaat',
'Työttömät, 2017 (PT) osuudesta asukkaat',
'Opiskelijat, 2017 (PT) osuudesta asukkaat',
'A Maatalous, metsätalous ja kalatalous, 2017 (TP) osuudesta asukkaat',
'J Informaatio ja viestintä, 2017 (TP) osuudesta asukkaat',
'K Rahoitus- ja vakuutustoiminta, 2017 (TP) osuudesta asukkaat',
'O Julkinen hallinto ja maanpuolustus; pakollinen sosiaalivakuutus, 2017 (TP) osuudesta asukkaat',
'P Koulutus, 2017 (TP) osuudesta asukkaat',
'Q Terveys- ja sosiaalipalvelut, 2017 (TP) osuudesta asukkaat',
'R Taiteet, viihde ja virkistys, 2017 (TP) osuudesta asukkaat',
'Ravintolat osuudesta asukkaat',
'Myymälät osuudesta asukkaat',
'Nuorten yksinasuvien taloudet, 2018 (TE) osuudesta taloudet',
'Lapsettomat nuorten parien taloudet, 2018 (TE) osuudesta taloudet',
'Lapsitaloudet, 2018 (TE) osuudesta taloudet',
'Aikuisten taloudet, 2018 (TE) osuudesta taloudet',
'Eläkeläisten taloudet, 2018 (TE) osuudesta taloudet',
'Omistusasunnoissa asuvat taloudet, 2018 (TE) osuudesta taloudet',
'Vuokra-asunnoissa asuvat taloudet, 2018 (TE) osuudesta taloudet',
'Alimpaan tuloluokkaan kuuluvat taloudet, 2017 (TR) osuudesta taloudet',
'Keskimmäiseen tuloluokkaan kuuluvat taloudet, 2017 (TR) osuudesta taloudet',
'Ylimpään tuloluokkaan kuuluvat taloudet, 2017 (TR) osuudesta taloudet',
'1.1.3 Kirkollisvero Yhteensä Saajien lukumäärä osuudesta verotetut',
'11. Yrittäjätulot yhteensä Yhteensä Saajien lukumäärä osuudesta verotetut',
'4.2.6 Tapaturma- ym. muut päivärahat Yhteensä Saajien lukumäärä osuudesta verotetut',
'4.2.7 Päivä- ja äitiyspäivärahat Yhteensä Saajien lukumäärä osuudesta verotetut',
'4.2.8 Lapsen kotihoidon tuki Yhteensä Saajien lukumäärä osuudesta verotetut',
'4.2.9 Kuntoutusraha ja -avustus Yhteensä Saajien lukumäärä osuudesta verotetut',
'5.1.3 Vuokratulo Yhteensä Saajien lukumäärä osuudesta verotetut',
'192.168.3.11 Asuntolainan korkomenot (TVL 58 § 2 mom.) Yhteensä Summa, euroa osuus total',
'14.12 Opintolainavähennys Yhteensä Summa, euroa osuus total',
'14.7 Kotitalousvähennys Yhteensä Summa, euroa osuus total',
'1.1 Tuloverot yhteensä Yhteensä Summa, euroa osuus total',
'4.1.C Autoetu Yhteensä Summa, euroa osuus total',
'4.2 Työmatkakulujen perusteella palkkatulosta vähennettävä määrä (TVL 93 §) Yhteensä Summa, euroa osuus total',
'5. Pääomatulot yhteensä Yhteensä Summa, euroa osuus total',
'Ruotsinkielisten osuus väestöstä, %, 2019',
'Taajama-aste, %, 2018',
'Ulkomaan kansalaisten osuus väestöstä, %, 2019',
'Kuntien välinen muuttovoitto/-tappio, henkilöä, 2019 osuudesta asukkaat nollatta',
'etaisyys osuus area_code', 'tkalue',
'Tuloveroprosentti']
col_puolueet = ['Ääniosuus KD',
'Ääniosuus KESK',
'Ääniosuus KOK',
'Ääniosuus PS',
'Ääniosuus RKP',
'Ääniosuus SDP',
'Ääniosuus VAS',
'Ääniosuus VIHR',
'Äänestysosuus']
if not pnroalue:
not_in_kunta = ['Myymälät osuudesta asukkaat', 'Postinumeroalueen pinta-ala osuus area_code', 'Kuntien välinen muuttovoitto/-tappio, henkilöä, 2019 osuudesta asukkaat nollatta', 'etaisyys osuus area_code', 'tkalue', 'Ravintolat osuudesta asukkaat']
removeset = set(not_in_kunta)
numeric_features[:] = [x for x in numeric_features if x not in removeset]
categorical_features=[]
if parties:
numeric_features = numeric_features + col_puolueet
return(numeric_features, categorical_features)
|
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2
] |
ithz/security.py
|
ergoithz/ithz
| 1 |
1602296
|
<reponame>ergoithz/ithz
# -*- coding: utf-8 -*-
from google.appengine.api import users
from ithz.data import *
class SectionRights(object):
view = None # level list
edit = None # level list
add = False # boolean
assign = False # boolean
def __init__(self):
self.view = [0]
self.edit = []
class BlogRights(object):
view = None # level list
viewEntry = None
edit = None # level list
editEntry = None
add = False # boolean
addEntry = False
assign = False # boolean
assignEntry = False
def __init__(self):
self.view = [0]
self.edit = []
self.viewEntry = [0]
self.editEntry = []
class UserRights(object):
edit = None # level list
list = False # boolean
add = False # boolean
def __init__(self):
self.edit = []
class FileRights(object):
add = False
admin = False
class ServerRights(object):
admin = False
class __level(object):
def __init__(self,id,one,more):
self.id = id
self.__one = one
self.__more = more
self.section = SectionRights()
self.blog = BlogRights()
self.user = UserRights()
self.file = FileRights()
self.server = ServerRights()
def getName(self,n=0):
if n:
return self.__more
return self.__one
levels = [
__level(0,"public user","public users"),
__level(1,"admin", "admins"),
__level(2,"moderator", "moderators"),
__level(3,"editor","editors"),
__level(4,"registered user", "registered users"),
]
''' Due GQL limitations, level's length cannot be greater than 30
From GQL reference:
[...]
Note: The IN and != operators use multiple queries behind the scenes.
For example, the IN operator executes a separate underlying datastore
query for every item in the list. The entities returned are a result of
the cross-product of all the underlying datastore queries and are
de-duplicated. A maximum of 30 datastore queries are allowed for any
single GQL query.
[...]
(http://code.google.com/intl/es/appengine/docs/python/datastore/gqlreference.html)
'''
__lvl = range(1,len(levels))+[0]
# Admin
__adm = __lvl[:]
levels[1].section.view = __adm
levels[1].section.edit = __adm[:-2]
levels[1].section.add = True
levels[1].section.assign = True
levels[1].blog.view = __adm
levels[1].blog.viewEntry = __adm
levels[1].blog.edit = __adm[:-2]
levels[1].blog.editEntry = __adm[:-2]
levels[1].blog.add = True
levels[1].blog.addEntry = True
levels[1].blog.assign = True
levels[1].blog.assignEntry = True
levels[1].user.list = True
levels[1].user.add = True
levels[1].user.edit = __adm
levels[1].file.add = True
levels[1].file.admin = True
levels[1].server.admin = True
# Mod
__mod = __lvl[:-1]
levels[2].section.view = __mod
levels[2].section.edit = __mod[:-2]
levels[2].section.add = True
levels[2].section.assign = True
levels[2].blog.view = __mod
levels[2].blog.viewEntry = __mod
levels[2].blog.edit = __mod[:-2]
levels[2].blog.editEntry = __mod[:-2]
levels[2].blog.add = True
levels[2].blog.addEntry = True
levels[2].blog.assign = True
levels[2].blog.assignEntry = True
levels[2].user.list = True
levels[2].user.add = True
levels[2].user.edit = __mod
levels[2].file.add = True
levels[2].file.admin = True
# Editor
levels[3].section.view = [3, 4, 0]
levels[3].section.edit = [3]
levels[3].section.add = True
levels[3].blog.view = [3, 4, 0]
levels[3].blog.viewEntry = [3, 4, 0]
levels[3].blog.edit = [3]
levels[3].blog.editEntry = [3]
levels[3].blog.add = True
levels[3].blog.addEntry = True
levels[3].file.add = True
# Registered user
levels[4].section.view = [4, 0]
levels[4].blog.view = [4, 0]
def getLevelName(id,n=0):
''' Returns level name of given id. '''
return levels[id].getName(n)
def getUserRef(email=None):
''' Returns user reference by its email '''
return users.User(email)
class User(object):
''' A class that represents an application user and it's rights and properties. '''
username = "Public user"
email = ""
ref = None
__level = None # Level class instance
def __init__(self,ref=None):
level = 0
if ref==None:
ref = users.get_current_user()
if ref:
self.username = ref.nickname()
self.email = ref.email()
self.ref = ref
if users.is_current_user_admin():
level = 1
else:
qu = getUserModel(ref)
if qu and qu.level != 0:
level = qu.level
self.__level = levels[level]
def __eq__(self,y):
''' x.__eq__(y) <==> x==y '''
return self.ref == y
def getLevel(self):
return self.__level.id
# Sections
def getSectionVisible(self):
''' Return list of visible section levels '''
return self.__level.section.view[:]
def getSectionEdit(self):
''' Return list of editable section levels '''
return self.__level.section.edit[:]
def getSectionAdd(self):
''' Return boolean. Can user add a section? '''
return self.__level.section.add
def getSectionAssign(self):
''' Return boolean. Can user reassign a section? '''
return self.__level.section.assign
# Blogs
def getBlogVisible(self):
''' Return list of visible blog levels '''
return self.__level.blog.view[:]
def getBlogEdit(self):
''' Return list of editable blog levels '''
return self.__level.blog.edit[:]
def getBlogAdd(self):
''' Return boolean. Can user add a blog? '''
return self.__level.blog.add
def getBlogAssign(self):
''' Return boolean. Can user reassign a blog? '''
return self.__level.blog.assign
def getBlogEntryVisible(self):
''' Return list of visible blog levels '''
return self.__level.blog.viewEntry[:]
def getBlogEntryEdit(self):
''' Return list of editable blog levels '''
return self.__level.blog.editEntry[:]
def getBlogEntryAdd(self):
''' Return boolean. Can user add a blog? '''
return self.__level.blog.addEntry
def getBlogEntryAssign(self):
''' Return boolean. Can user reassign a blog? '''
return self.__level.blog.assignEntry
# Users
def getUserAdd(self):
''' Return boolean. Can user reassign a section? '''
return self.__level.user.add
def getUserEdit(self):
''' Return list of editable users' levels '''
return self.__level.user.edit[:]
def getUserListing(self):
''' Return boolean. Can user list all users? '''
return self.__level.user.list
def getLevel(self):
''' Return level id. '''
return self.__level.id
def getLevelOver(self):
''' Return level over own level '''
if not self.__level.id in (0, 1):
return self.__level.id-1
return None
def getLevelName(self,n=0):
''' Return level verbose name. '''
return self.__level.getName(n)
# Files
def getFileAdd(self):
''' Return boolean. Can user add a file? '''
return self.__level.file.add
def getFileAdmin(self):
''' Return boolean. Can user administrate files? '''
return self.__level.file.admin
# Server
def getServerAdmin(self):
''' Return boolean. Can user flush the cache? '''
return self.__level.server.admin
# What is?
def ispublic(self):
''' Returns True user is public, otherwise False '''
return (self.__level.id == 0)
def isadmin(self):
''' Returns True user is admin, otherwise False '''
return (self.__level.id == 1)
def ismoderator(self):
''' Returns True user is moderator, otherwise False '''
return (self.__level.id == 2)
def iseditor(self):
''' Returns True user is editor, otherwise False '''
return (self.__level.id == 3)
def isregistrered(self):
''' Returns True user is registered, otherwise False '''
return (self.__level.id == 4)
|
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2
] |
pydocusign/test.py
|
enefeq/pydocusign
| 10 |
181484
|
<filename>pydocusign/test.py
"""Utilities to run tests around `pydocusign`."""
import os
def fixtures_dir():
"""Return absolute path to `pydocusign`'s fixtures dir, as best guess.
This function supports two situations:
* you use it in code repository's root, i.e. ``fixtures/`` folder is in the
same folder than ``pydocusign/test.py``.
* `tox` runs the documentation build, i.e. ``fixtures/`` folder is in the
code repository's root, whereas ``pydocusign/test.py`` lives somewhere in
``.tox/``.
Other situations are not supported at the moment.
"""
here = os.path.dirname(os.path.abspath(__file__))
here_parts = here.split(os.path.sep)
is_tox = '.tox' in here_parts
if is_tox:
tox_index = here_parts.index('.tox')
project_dir = here
for i in range(len(here_parts) - tox_index):
project_dir = os.path.dirname(project_dir)
else:
project_dir = os.path.dirname(here)
return os.path.normpath(os.path.join(project_dir, 'fixtures'))
|
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] |
allocator.py
|
javicacheiro/resource-allocator
| 0 |
86690
|
"""Resource Allocation API"""
import kvstore
PREFIX = 'resources'
DISK_PREFIX = 'disks'
ADDRESS_PREFIX = 'networks'
class ResourceManager(object):
"""Resource Management base class"""
def __init__(self, endpoint='http://127.0.0.1:8500/v1/kv'):
self.endpoint = endpoint
self._kv = kvstore.Client(self.endpoint)
def get_status(self, resource_status_path):
"""Returns the status of all the resources of a given node"""
subtree = self._kv.recurse(
'{}/{}'.format(PREFIX, resource_status_path))
return {self._parse_res_name(k):
subtree[k] for k in subtree.keys() if subtree[k] != ""}
def set_status(self, resource_status_path, status):
"""Sets the status of a given resource"""
self._kv.set(
'{}/{}/status'.format(PREFIX, resource_status_path), status)
def get_free(self, resource_status_path):
"""Returns the list of free resources of a given node"""
subtree = self._kv.recurse('{}/{}'.format(PREFIX, resource_status_path))
free_res = self._filter_res(subtree, status='free')
return free_res
def get_used(self, resource_status_path):
"""Returns the list of used resources of a given node"""
subtree = self._kv.recurse('{}/{}'.format(PREFIX, resource_status_path))
used_res = self._filter_res(subtree, status='used')
return used_res
def _parse_res_name(self, key):
"""Extract disk name from key"""
return key.split('/')[-2]
def _filter_res(self, subtree, status='free'):
"""Filter resources based on status"""
if status == 'free':
filtered_res = [self._parse_res_name(k)
for k in sorted(subtree.keys())
if subtree[k] == 'free']
elif status == 'used':
filtered_res = [self._parse_res_name(k)
for k in sorted(subtree.keys())
# Skip also the empty key (tree directory)
if subtree[k] != 'free' and subtree[k] != ""]
else:
raise StatusNotSupportedError()
return filtered_res
class DiskManager(ResourceManager):
"""Manage disk allocations"""
def get_used(self, node):
return ResourceManager.get_used(self, DISK_PREFIX + "/" + node)
def get_free(self, node):
disks = ResourceManager.get_free(self, DISK_PREFIX + "/" + node)
return {'number': len(disks), "disks": disks}
def get_status(self, node, disk):
return ResourceManager.get_status(self, DISK_PREFIX + "/" + node + "/" + disk)
def set_status(self, node, disk, status):
return ResourceManager.set_status(self, DISK_PREFIX + "/" + node + "/" + disk, status)
def set_used(self, node, disk):
return self.set_status(node, disk, "used")
def set_free(self, node, disk):
return self.set_status(node, disk, "free")
class AddressManager(ResourceManager):
"""Manage network address allocations"""
def get_used(self, network):
return ResourceManager.get_used(self, ADDRESS_PREFIX + "/" + network)
def get_free(self, network):
ips = ResourceManager.get_free(self, ADDRESS_PREFIX + "/" + network)
return {'number': len(ips), "ips": ips}
def get_status(self, network, ip):
return ResourceManager.get_status(self, ADDRESS_PREFIX + network + "/" + ip)
def set_status(self, network, ip, status):
return ResourceManager.set_status(self, ADDRESS_PREFIX + network + "/" + ip, status)
def set_used(self, network, ip):
return self.set_status(network, ip, "used")
def set_free(self, network, ip):
return self.set_status(network, ip, "free")
class StatusNotSupportedError(Exception):
pass
|
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] |
eda/numerical_data/correlation_heat_map.py
|
kmsk99/data_science_toolbar
| 0 |
150345
|
corr_data = df_train[['Id', 'MSSubClass', 'LotFrontage', 'LotArea', 'OverallQual',
'OverallCond', 'YearBuilt', 'YearRemodAdd', 'MasVnrArea', 'BsmtFinSF1',
'BsmtFinSF2', 'BsmtUnfSF', 'TotalBsmtSF', '1stFlrSF', '2ndFlrSF',
'LowQualFinSF', 'GrLivArea', 'BsmtFullBath', 'BsmtHalfBath', 'FullBath',
'HalfBath', 'BedroomAbvGr', 'KitchenAbvGr', 'TotRmsAbvGrd',
'Fireplaces', 'GarageYrBlt', 'GarageCars', 'GarageArea', 'WoodDeckSF',
'OpenPorchSF', 'EnclosedPorch', '3SsnPorch', 'ScreenPorch', 'PoolArea',
'MiscVal', 'MoSold', 'YrSold', 'SalePrice']]
colormap = plt.cm.PuBu
sns.set(font_scale=1.0)
f, ax = plt.subplots(figsize=(14, 12))
plt.title('Correlation of Numeric Features with Sale Price', y=1, size=18)
sns.heatmap(corr_data.corr(), square=True, linewidths=0.1,
cmap=colormap, linecolor="white", vmax=0.8)
# Heat Map은 seaborn 덕분에 직관적으로 이해가 가능하여 변수 간 상관관계에 대하여 쉽게 알 수 있습니다.
# 또한 변수 간 다중 공선성을 감지하는 데 유용합니다.
# 대각선 열을 제외한 박스 중 가장 진한 파란색을 띄는 박스가 보입니다.
# 첫 번째는 'TotalBsmtSF'와 '1stFlrSF'변수의 관계입니다.
# 두 번째는 'Garage'와 관련한 변수를 나타냅니다.
# 두 경우 모두 변수 사이의 상관 관계가 너무 강하여 다중 공선성(MultiColarisity) 상황이 나타날 수 있습니다.
# 변수가 거의 동일한 정보를 제공하므로 다중 공선성이 실제로 발생한다는 결론을 내릴 수 있습니다.
# 또한 확인해야할 부분은 'SalePrice'와의 상관 관계입니다.
# 'GrLivArea', 'TotalBsmtSF'및 'OverallQual'은 큰 관계를 보입니다.
# 나머지 변수와의 상관 관계를 자세히 알아보기 위해 Zoomed Heat Map을 확인합니다.
|
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] |
utils/indicators/cost_abat_value/calculate.py
|
kvanderwijst/IAMDiagnostics
| 0 |
77451
|
import numpy as np
from utils.data.handling import set_value_from_var_column
def create_columns(
data,
prev_meta,
years=["2050", "2100"],
cprices=[130.3, 1441.3],
policy_scenario="diag-c80-gr5",
mute=False,
):
meta = prev_meta.copy()
created_columns = []
## Calculate policy cost for each model
for year, cprice in zip(years, cprices):
col_costs = f"Policy cost {year}"
col_emiss = f"Emissions CAV {year}"
col_emiss_base = f"{col_emiss} base"
set_value_from_var_column(
data, meta, "Policy cost variable", col_costs, year, policy_scenario
)
set_value_from_var_column(
data, meta, "Emissions_for_CAV", col_emiss, year, policy_scenario
)
set_value_from_var_column(
data, meta, "Emissions_for_CAV", col_emiss_base, year, "diag-base"
)
# Make all costs positive
meta[col_costs] = meta[col_costs].abs()
# Cost per GDP
col_per_GDP = f"{col_costs} per GDP"
GDP_column = f"GDP {year} {policy_scenario}"
# Check if GDP column already exists
if GDP_column not in meta.columns:
set_value_from_var_column(
data, meta, "GDP_metric", GDP_column, year, policy_scenario
)
meta[col_per_GDP] = meta[col_costs] / meta[GDP_column]
# Using this information, calculate CAV
col_CAV = f"CAV {year}"
GHG_reduction_absolute = (
meta[col_emiss_base] - meta[col_emiss]
) / 1000 # Convert Mt to Gt
# cprice = meta[f'Carbon price c80 {year}'] ## Use hardcoded c-price
meta[col_CAV] = meta[col_costs] / (GHG_reduction_absolute * cprice)
# CAV < 0 or CAV > 2.5 is a mistake, exclude those
meta.loc[
~meta[col_CAV].between(0, 2.5)
| (meta[col_per_GDP] > 0.15)
| (meta[col_costs] > 50000),
[col_costs, col_per_GDP, col_CAV, col_per_GDP],
] = np.nan
created_columns += [col_costs, col_emiss, col_emiss_base, col_per_GDP, col_CAV]
if not mute:
print(f"Created columns: {created_columns}\n")
return meta
|
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12700,
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2
] |
Day9b/Day9b.py
|
mathuin/advent-of-code-2017
| 0 |
152680
|
#!/usr/bin/python3
import sys
# stream processing
# streams have groups and garbage.
# groups are delimited by {}.
# groups can contain other groups and garbage.
# garbage is delimited by <>.
# garbage can't contain groups.
# any character followed by ! is cancelled.
# goal is to find total score for all groups.
# group score = 1 + score of containing group.
# step one, remove all the !. from the stream.
def cancelChars(chars):
for k, v in enumerate(chars):
if v == '':
continue
if v == '!':
chars[k] = ''
chars[k+1] = ''
return [x for x in chars if x != '']
# step two, remove all the <[^>]> from the stream.
def removeGarbage(chars):
trash = False
removed = 0
for char in chars:
if not trash and char == '<':
trash = True
removed -= 1
if trash and char == '>':
trash = False
if trash:
removed += 1
return removed
def countGroups(chars):
groups = []
depth = 0
for k, v in enumerate(chars):
if v == '{':
depth += 1
if v == '}':
groups.append(depth)
depth -= 1
return sum(groups)
# Unit tests for countGroups
tt = {
'a': ('<>', 0),
'b': ('<random characters>', 17),
'c': ('<<<<>', 3),
'd': ('<{!>}>', 2),
'e': ('<!!>', 0),
'f': ('<!!!>>', 0),
'g': ('<{o"i!a,<{i<a>', 10),
}
for k, v in tt.items():
chars = list(v[0])
chars = cancelChars(chars)
result = removeGarbage(chars)
if result != v[1]:
print("FAIL: input ", v[0], ": expected ", v[1], ", got ", result, sep="")
# The input is not checked for sanity, just existence.
stream = sys.stdin.readlines()
if len(stream) == 0:
print("stream missing!")
sys.exit(1)
chars = list(stream[0])
chars = cancelChars(chars)
print(removeGarbage(chars))
|
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279,
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29898,
305,
1503,
876,
13,
2
] |
python/modules/mysql_server.py
|
91-jinrong/-91_monitor
| 1 |
7779
|
<filename>python/modules/mysql_server.py
#!/bin/env python
#-*-coding:utf-8-*-
import os
import sys
import string
import time
import datetime
import MySQLdb
class MySQL:
def __int__(self,host,port,user,passwd,dbname,timeout,charset):
self.host = host
self.port = port
self.user = user
self.passwd = <PASSWORD>
self.dbname = test
self.timeout = timeout
self.charset = charset
def db_connect(self):
connect=MySQLdb.connect(host=self.host,user=self.user,passwd=<PASSWORD>,port=int(self.port),connect_timeout=int(self.timeout),charset=self.charset)
return connect
def execute(self,sql,param):
conn=MySQLdb.connect(host=self.host,user=self.user,passwd=<PASSWORD>,port=int(self.port),connect_timeout=int(self.timeout),charset=self.charset)
conn.select_db(self.dbname)
cursor = conn.cursor()
if param <> '':
cursor.execute(sql,param)
else:
cursor.execute(sql)
conn.commit()
cursor.close()
conn.close()
def query(self,sql):
conn=MySQLdb.connect(host=self.host,user=self.user,passwd=<PASSWORD>,port=int(self.port),connect_timeout=int(self.timeout),charset=self.charset)
conn.select_db(self.dbname)
cursor = conn.cursor()
count=cursor.execute(sql)
if count == 0 :
result=0
else:
result=cursor.fetchall()
return result
cursor.close()
conn.close()
def get_option(self,key):
conn=MySQLdb.connect(host=self.host,user=self.user,passwd=<PASSWORD>,port=int(self.port),connect_timeout=int(self.timeout),charset=self.charset)
conn.select_db(self.dbname)
cursor = conn.cursor()
sql="select value from options where name=+'"+key+"'"
count=cursor.execute(sql)
if count == 0 :
result=0
else:
result=cursor.fetchone()
return result[0]
cursor.close()
conn.close()
|
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13,
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14708,
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29914,
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29922,
29966,
25711,
17013,
10202,
637,
29922,
524,
29898,
1311,
29889,
637,
511,
6915,
29918,
15619,
29922,
524,
29898,
1311,
29889,
15619,
511,
3090,
842,
29922,
1311,
29889,
3090,
842,
29897,
13,
4706,
11009,
29889,
2622,
29918,
2585,
29898,
1311,
29889,
2585,
978,
29897,
13,
4706,
10677,
353,
11009,
29889,
18127,
580,
13,
4706,
565,
1828,
15271,
525,
2396,
13,
9651,
10677,
29889,
7978,
29898,
2850,
29892,
3207,
29897,
13,
4706,
1683,
29901,
13,
9651,
10677,
29889,
7978,
29898,
2850,
29897,
13,
4706,
11009,
29889,
15060,
580,
13,
4706,
10677,
29889,
5358,
580,
13,
4706,
11009,
29889,
5358,
580,
13,
13,
1678,
822,
2346,
29898,
1311,
29892,
2850,
1125,
13,
4706,
11009,
29922,
3421,
4176,
2585,
29889,
6915,
29898,
3069,
29922,
1311,
29889,
3069,
29892,
1792,
29922,
1311,
29889,
1792,
29892,
3364,
9970,
29922,
29966,
25711,
17013,
10202,
637,
29922,
524,
29898,
1311,
29889,
637,
511,
6915,
29918,
15619,
29922,
524,
29898,
1311,
29889,
15619,
511,
3090,
842,
29922,
1311,
29889,
3090,
842,
29897,
13,
4706,
11009,
29889,
2622,
29918,
2585,
29898,
1311,
29889,
2585,
978,
29897,
13,
4706,
10677,
353,
11009,
29889,
18127,
580,
13,
4706,
2302,
29922,
18127,
29889,
7978,
29898,
2850,
29897,
13,
4706,
565,
2302,
1275,
29871,
29900,
584,
13,
9651,
1121,
29922,
29900,
13,
4706,
1683,
29901,
13,
9651,
1121,
29922,
18127,
29889,
9155,
497,
580,
13,
4706,
736,
1121,
13,
4706,
10677,
29889,
5358,
580,
13,
4706,
11009,
29889,
5358,
580,
13,
13,
1678,
822,
679,
29918,
3385,
29898,
1311,
29892,
1989,
1125,
13,
4706,
11009,
29922,
3421,
4176,
2585,
29889,
6915,
29898,
3069,
29922,
1311,
29889,
3069,
29892,
1792,
29922,
1311,
29889,
1792,
29892,
3364,
9970,
29922,
29966,
25711,
17013,
10202,
637,
29922,
524,
29898,
1311,
29889,
637,
511,
6915,
29918,
15619,
29922,
524,
29898,
1311,
29889,
15619,
511,
3090,
842,
29922,
1311,
29889,
3090,
842,
29897,
13,
4706,
11009,
29889,
2622,
29918,
2585,
29898,
1311,
29889,
2585,
978,
29897,
13,
4706,
10677,
353,
11009,
29889,
18127,
580,
13,
4706,
4576,
543,
2622,
995,
515,
3987,
988,
1024,
29922,
29974,
11838,
29974,
1989,
13578,
11838,
13,
4706,
2302,
29922,
18127,
29889,
7978,
29898,
2850,
29897,
13,
4706,
565,
2302,
1275,
29871,
29900,
584,
13,
9651,
1121,
29922,
29900,
13,
4706,
1683,
29901,
13,
9651,
1121,
29922,
18127,
29889,
9155,
650,
580,
13,
4706,
736,
1121,
29961,
29900,
29962,
13,
4706,
10677,
29889,
5358,
580,
13,
4706,
11009,
29889,
5358,
580,
29871,
13,
2
] |
hsi/log.py
|
morrocoy/hsi
| 1 |
197992
|
# -*- coding: utf-8 -*-
""" An interface module for mcx (Monte Carlo eXtreme).
Created on Tue Mar 16 21:43:18 2021
@author: kpapke
"""
import logging
class LogManager(object):
""" Class to manage multiple loggers from different modules
"""
def __init__(self):
""" Constructor. """
self.level = logging.WARNING
self.consoleHandler = logging.StreamHandler()
self.consoleHandler.setLevel(self.level)
self.formatter = logging.Formatter(
"%(asctime)s %(filename)35s: %(lineno)-4d: %(funcName)20s(): " \
"%(levelname)-7s: %(message)s")
self.consoleHandler.setFormatter(self.formatter)
self.loggers = {}
def getLogger(self, module_name):
""" Return a logger for a module.
"""
log = logging.getLogger(module_name)
log.setLevel(self.level)
log.addHandler(self.consoleHandler)
self.loggers[module_name] = log
return log
def setLevel(self, level):
""" Set the log level for all loggers that have been created.
"""
self.level = level
self.consoleHandler.setLevel(level)
for log in self.loggers.values():
log.setLevel(level)
logmanager = LogManager()
|
[
1,
396,
448,
29930,
29899,
14137,
29901,
23616,
29899,
29947,
448,
29930,
29899,
13,
15945,
29908,
530,
5067,
3883,
363,
286,
18904,
313,
7185,
371,
15021,
321,
29990,
2484,
1004,
467,
13,
13,
20399,
373,
323,
434,
1085,
29871,
29896,
29953,
29871,
29906,
29896,
29901,
29946,
29941,
29901,
29896,
29947,
29871,
29906,
29900,
29906,
29896,
13,
13,
29992,
8921,
29901,
413,
29886,
481,
446,
13,
15945,
29908,
13,
5215,
12183,
13,
13,
13,
1990,
4522,
3260,
29898,
3318,
1125,
13,
1678,
9995,
4134,
304,
10933,
2999,
1480,
5743,
515,
1422,
10585,
13,
1678,
9995,
13,
13,
1678,
822,
4770,
2344,
12035,
1311,
1125,
13,
4706,
9995,
1281,
18769,
29889,
9995,
13,
4706,
1583,
29889,
5563,
353,
12183,
29889,
29956,
25614,
13,
13,
4706,
1583,
29889,
11058,
4598,
353,
12183,
29889,
3835,
4598,
580,
13,
4706,
1583,
29889,
11058,
4598,
29889,
842,
10108,
29898,
1311,
29889,
5563,
29897,
13,
13,
4706,
1583,
29889,
689,
2620,
353,
12183,
29889,
18522,
29898,
13,
9651,
11860,
29898,
294,
312,
603,
29897,
29879,
1273,
29898,
9507,
29897,
29941,
29945,
29879,
29901,
1273,
29898,
1915,
8154,
6817,
29946,
29881,
29901,
1273,
29898,
9891,
1170,
29897,
29906,
29900,
29879,
7295,
376,
320,
13,
9651,
11860,
29898,
5563,
978,
6817,
29955,
29879,
29901,
1273,
29898,
4906,
29897,
29879,
1159,
13,
4706,
1583,
29889,
11058,
4598,
29889,
842,
18522,
29898,
1311,
29889,
689,
2620,
29897,
13,
13,
4706,
1583,
29889,
1188,
5743,
353,
6571,
13,
13,
1678,
822,
679,
16363,
29898,
1311,
29892,
3883,
29918,
978,
1125,
13,
4706,
9995,
7106,
263,
17927,
363,
263,
3883,
29889,
13,
4706,
9995,
13,
4706,
1480,
353,
12183,
29889,
657,
16363,
29898,
5453,
29918,
978,
29897,
13,
4706,
1480,
29889,
842,
10108,
29898,
1311,
29889,
5563,
29897,
13,
4706,
1480,
29889,
1202,
4598,
29898,
1311,
29889,
11058,
4598,
29897,
13,
4706,
1583,
29889,
1188,
5743,
29961,
5453,
29918,
978,
29962,
353,
1480,
13,
4706,
736,
1480,
13,
13,
1678,
822,
731,
10108,
29898,
1311,
29892,
3233,
1125,
13,
4706,
9995,
3789,
278,
1480,
3233,
363,
599,
1480,
5743,
393,
505,
1063,
2825,
29889,
13,
4706,
9995,
13,
4706,
1583,
29889,
5563,
353,
3233,
13,
4706,
1583,
29889,
11058,
4598,
29889,
842,
10108,
29898,
5563,
29897,
13,
4706,
363,
1480,
297,
1583,
29889,
1188,
5743,
29889,
5975,
7295,
13,
9651,
1480,
29889,
842,
10108,
29898,
5563,
29897,
13,
13,
13,
1188,
12847,
353,
4522,
3260,
580,
13,
2
] |
tests/test_timeparser.py
|
vgoehler/python-i3-battery-block
| 0 |
2570
|
<gh_stars>0
from datetime import time
import pytest
from i3_battery_block_vgg.timeparser import __parse_time_manually
from i3_battery_block_vgg.timeparser import parse_time
@pytest.mark.parametrize(
"time_input, expected",
[
("12:13", time(hour=12, minute=13)),
("12:13:14", time(hour=12, minute=13, second=14)),
('00:54:00', time(hour=0, minute=54, second=0))
]
)
def test_manually_time_parsing(time_input: str, expected: time):
assert __parse_time_manually(time_input) == expected, "manual time parsing has gone wrong"
@pytest.mark.parametrize(
"time_input, expected",
[
("12:13", time(hour=12, minute=13)),
("12:13:14", time(hour=12, minute=13, second=14)),
('00:54:00', time(hour=0, minute=54, second=0))
]
)
def test_time_parsing(time_input: str, expected: time):
assert parse_time(time_input) == expected, "time parsing has gone wrong"
|
[
1,
529,
12443,
29918,
303,
1503,
29958,
29900,
13,
3166,
12865,
1053,
931,
13,
13,
5215,
11451,
1688,
13,
13,
3166,
474,
29941,
29918,
29890,
2620,
29891,
29918,
1271,
29918,
29894,
1505,
29889,
2230,
16680,
1053,
4770,
5510,
29918,
2230,
29918,
1171,
1474,
13,
3166,
474,
29941,
29918,
29890,
2620,
29891,
29918,
1271,
29918,
29894,
1505,
29889,
2230,
16680,
1053,
6088,
29918,
2230,
13,
13,
13,
29992,
2272,
1688,
29889,
3502,
29889,
3207,
300,
374,
911,
29898,
13,
1678,
376,
2230,
29918,
2080,
29892,
3806,
613,
13,
1678,
518,
13,
4706,
4852,
29896,
29906,
29901,
29896,
29941,
613,
931,
29898,
18721,
29922,
29896,
29906,
29892,
11015,
29922,
29896,
29941,
8243,
13,
4706,
4852,
29896,
29906,
29901,
29896,
29941,
29901,
29896,
29946,
613,
931,
29898,
18721,
29922,
29896,
29906,
29892,
11015,
29922,
29896,
29941,
29892,
1473,
29922,
29896,
29946,
8243,
13,
4706,
6702,
29900,
29900,
29901,
29945,
29946,
29901,
29900,
29900,
742,
931,
29898,
18721,
29922,
29900,
29892,
11015,
29922,
29945,
29946,
29892,
1473,
29922,
29900,
876,
13,
1678,
4514,
13,
29897,
13,
1753,
1243,
29918,
1171,
1474,
29918,
2230,
29918,
862,
2976,
29898,
2230,
29918,
2080,
29901,
851,
29892,
3806,
29901,
931,
1125,
13,
1678,
4974,
4770,
5510,
29918,
2230,
29918,
1171,
1474,
29898,
2230,
29918,
2080,
29897,
1275,
3806,
29892,
376,
11288,
931,
13755,
756,
7695,
2743,
29908,
13,
13,
13,
29992,
2272,
1688,
29889,
3502,
29889,
3207,
300,
374,
911,
29898,
13,
1678,
376,
2230,
29918,
2080,
29892,
3806,
613,
13,
1678,
518,
13,
4706,
4852,
29896,
29906,
29901,
29896,
29941,
613,
931,
29898,
18721,
29922,
29896,
29906,
29892,
11015,
29922,
29896,
29941,
8243,
13,
4706,
4852,
29896,
29906,
29901,
29896,
29941,
29901,
29896,
29946,
613,
931,
29898,
18721,
29922,
29896,
29906,
29892,
11015,
29922,
29896,
29941,
29892,
1473,
29922,
29896,
29946,
8243,
13,
4706,
6702,
29900,
29900,
29901,
29945,
29946,
29901,
29900,
29900,
742,
931,
29898,
18721,
29922,
29900,
29892,
11015,
29922,
29945,
29946,
29892,
1473,
29922,
29900,
876,
13,
1678,
4514,
13,
29897,
13,
1753,
1243,
29918,
2230,
29918,
862,
2976,
29898,
2230,
29918,
2080,
29901,
851,
29892,
3806,
29901,
931,
1125,
13,
1678,
4974,
6088,
29918,
2230,
29898,
2230,
29918,
2080,
29897,
1275,
3806,
29892,
376,
2230,
13755,
756,
7695,
2743,
29908,
13,
2
] |
tests/test_code/py/chained/chained.py
|
FreddyZeng/code2flow
| 2,248 |
170675
|
class Chain():
def __init__(self, val):
self.val = val
def add(self, b):
self.val += b
return self
def sub(self, b):
self.val -= b
return self
def mul(self, b):
self.val *= b
return self
print(Chain(5).add(5).sub(2).mul(10))
|
[
1,
770,
678,
475,
7295,
13,
1678,
822,
4770,
2344,
12035,
1311,
29892,
659,
1125,
13,
4706,
1583,
29889,
791,
353,
659,
13,
13,
1678,
822,
788,
29898,
1311,
29892,
289,
1125,
13,
4706,
1583,
29889,
791,
4619,
289,
13,
4706,
736,
1583,
13,
13,
1678,
822,
1014,
29898,
1311,
29892,
289,
1125,
13,
4706,
1583,
29889,
791,
22361,
289,
13,
4706,
736,
1583,
13,
13,
1678,
822,
15065,
29898,
1311,
29892,
289,
1125,
13,
4706,
1583,
29889,
791,
334,
29922,
289,
13,
4706,
736,
1583,
13,
13,
13,
2158,
29898,
14688,
29898,
29945,
467,
1202,
29898,
29945,
467,
1491,
29898,
29906,
467,
16109,
29898,
29896,
29900,
876,
13,
2
] |
server/website/context.py
|
eIGato/mahjong-portal
| 10 |
76184
|
<reponame>eIGato/mahjong-portal
from django.conf import settings
from django.utils import timezone
from django.utils.translation import get_language
def context(request):
language = get_language()
return {
"GOOGLE_VERIFICATION_CODE": settings.GOOGLE_VERIFICATION_CODE,
"YANDEX_VERIFICATION_CODE": settings.YANDEX_VERIFICATION_CODE,
"SCHEME": settings.SCHEME,
"SHORT_DATE_FORMAT": language == "ru" and "d.m.Y" or "Y-m-d",
"CURRENT_YEAR": timezone.now().year,
}
|
[
1,
529,
276,
1112,
420,
29958,
29872,
6259,
1219,
29914,
655,
29882,
29926,
549,
29899,
25089,
13,
3166,
9557,
29889,
5527,
1053,
6055,
13,
3166,
9557,
29889,
13239,
1053,
29431,
13,
3166,
9557,
29889,
13239,
29889,
3286,
18411,
1053,
679,
29918,
11675,
13,
13,
13,
1753,
3030,
29898,
3827,
1125,
13,
1678,
4086,
353,
679,
29918,
11675,
580,
13,
13,
1678,
736,
426,
13,
4706,
376,
17080,
29949,
29954,
1307,
29918,
5348,
6545,
28541,
29918,
16524,
1115,
6055,
29889,
17080,
29949,
29954,
1307,
29918,
5348,
6545,
28541,
29918,
16524,
29892,
13,
4706,
376,
29979,
2190,
19577,
29918,
5348,
6545,
28541,
29918,
16524,
1115,
6055,
29889,
29979,
2190,
19577,
29918,
5348,
6545,
28541,
29918,
16524,
29892,
13,
4706,
376,
29903,
3210,
29923,
2303,
1115,
6055,
29889,
29903,
3210,
29923,
2303,
29892,
13,
4706,
376,
7068,
8476,
29918,
6248,
29918,
19094,
1299,
1115,
4086,
1275,
376,
582,
29908,
322,
376,
29881,
29889,
29885,
29889,
29979,
29908,
470,
376,
29979,
29899,
29885,
29899,
29881,
613,
13,
4706,
376,
22484,
29450,
29918,
29979,
26441,
1115,
29431,
29889,
3707,
2141,
6360,
29892,
13,
1678,
500,
13,
2
] |
src/pywink/test/devices/sensor_test.py
|
vickyg3/python-wink
| 0 |
77072
|
<gh_stars>0
import json
import os
import unittest
from unittest.mock import MagicMock
from pywink.api import get_devices_from_response_dict
from pywink.devices import types as device_types
from pywink.devices.piggy_bank import WinkPorkfolioBalanceSensor
from pywink.devices.smoke_detector import WinkSmokeDetector, WinkCoDetector, WinkSmokeSeverity, WinkCoSeverity
class SensorTests(unittest.TestCase):
def setUp(self):
super(SensorTests, self).setUp()
self.api_interface = MagicMock()
all_devices = os.listdir('{}/api_responses/'.format(os.path.dirname(__file__)))
self.response_dict = {}
device_list = []
for json_file in all_devices:
if os.path.isfile('{}/api_responses/{}'.format(os.path.dirname(__file__), json_file)):
_json_file = open('{}/api_responses/{}'.format(os.path.dirname(__file__), json_file))
device_list.append(json.load(_json_file))
_json_file.close()
self.response_dict["data"] = device_list
def test_capability_should_not_be_none(self):
devices = get_devices_from_response_dict(self.response_dict, device_types.SENSOR_POD)
for device in devices:
self.assertIsNotNone(device.capability())
def test_tamper_detected_should_be_false(self):
devices = get_devices_from_response_dict(self.response_dict, device_types.SENSOR_POD)
for device in devices:
self.assertFalse(device.tamper_detected())
def test_unit_is_valid(self):
devices = get_devices_from_response_dict(self.response_dict, device_types.SENSOR_POD)
for device in devices:
if device.unit_type() == "boolean":
self.assertIsNone(device.unit())
else:
self.assertIsNotNone(device.unit())
class EggtrayTests(unittest.TestCase):
def setUp(self):
super(EggtrayTests, self).setUp()
self.api_interface = MagicMock()
all_devices = os.listdir('{}/api_responses/'.format(os.path.dirname(__file__)))
self.response_dict = {}
device_list = []
for json_file in all_devices:
if os.path.isfile('{}/api_responses/{}'.format(os.path.dirname(__file__), json_file)):
_json_file = open('{}/api_responses/{}'.format(os.path.dirname(__file__), json_file))
device_list.append(json.load(_json_file))
_json_file.close()
self.response_dict["data"] = device_list
def test_state_should_be_2(self):
devices = get_devices_from_response_dict(self.response_dict, device_types.EGGTRAY)
for device in devices:
self.assertEqual(device.state(), 2)
def test_capability_is_none(self):
devices = get_devices_from_response_dict(self.response_dict, device_types.EGGTRAY)
for device in devices:
self.assertEqual(device.capability(), None)
def test_unit_is_eggs(self):
devices = get_devices_from_response_dict(self.response_dict, device_types.EGGTRAY)
for device in devices:
self.assertEqual(device.unit(), "eggs")
class KeyTests(unittest.TestCase):
def setUp(self):
super(KeyTests, self).setUp()
self.api_interface = MagicMock()
all_devices = os.listdir('{}/api_responses/'.format(os.path.dirname(__file__)))
self.response_dict = {}
device_list = []
for json_file in all_devices:
if os.path.isfile('{}/api_responses/{}'.format(os.path.dirname(__file__), json_file)):
_json_file = open('{}/api_responses/{}'.format(os.path.dirname(__file__), json_file))
device_list.append(json.load(_json_file))
_json_file.close()
self.response_dict["data"] = device_list
def test_state_should_be_false(self):
devices = get_devices_from_response_dict(self.response_dict, device_types.KEY)
self.assertEqual(len(devices), 1)
for device in devices:
self.assertFalse(device.state())
def test_parent_id_should_not_be_none(self):
devices = get_devices_from_response_dict(self.response_dict, device_types.KEY)
for device in devices:
self.assertIsNotNone(device.parent_id())
def test_availble_is_true(self):
devices = get_devices_from_response_dict(self.response_dict, device_types.KEY)
for device in devices:
self.assertTrue(device.available())
def test_capability_is_activity_detected(self):
devices = get_devices_from_response_dict(self.response_dict, device_types.KEY)
for device in devices:
self.assertEqual(device.capability(), "activity_detected")
def test_unit_is_none(self):
devices = get_devices_from_response_dict(self.response_dict, device_types.KEY)
for device in devices:
self.assertIsNone(device.unit())
class PorkfolioTests(unittest.TestCase):
def setUp(self):
super(PorkfolioTests, self).setUp()
self.api_interface = MagicMock()
all_devices = os.listdir('{}/api_responses/'.format(os.path.dirname(__file__)))
self.response_dict = {}
device_list = []
for json_file in all_devices:
if os.path.isfile('{}/api_responses/{}'.format(os.path.dirname(__file__), json_file)):
_json_file = open('{}/api_responses/{}'.format(os.path.dirname(__file__), json_file))
device_list.append(json.load(_json_file))
_json_file.close()
self.response_dict["data"] = device_list
def test_unit_is_usd(self):
devices = get_devices_from_response_dict(self.response_dict, device_types.PIGGY_BANK)
self.assertEqual(len(devices), 2)
for device in devices:
if isinstance(device, WinkPorkfolioBalanceSensor):
self.assertEqual(device.unit(), "USD")
def test_capability_is_balance(self):
devices = get_devices_from_response_dict(self.response_dict, device_types.PIGGY_BANK)
for device in devices:
if isinstance(device, WinkPorkfolioBalanceSensor):
self.assertEqual(device.capability(), "balance")
def test_state_is_180(self):
devices = get_devices_from_response_dict(self.response_dict, device_types.PIGGY_BANK)
for device in devices:
if isinstance(device, WinkPorkfolioBalanceSensor):
self.assertEqual(device.state(), 180)
def test_available_is_true(self):
devices = get_devices_from_response_dict(self.response_dict, device_types.PIGGY_BANK)
for device in devices:
if isinstance(device, WinkPorkfolioBalanceSensor):
self.assertTrue(device.available())
class GangTests(unittest.TestCase):
def setUp(self):
super(GangTests, self).setUp()
self.api_interface = MagicMock()
all_devices = os.listdir('{}/api_responses/'.format(os.path.dirname(__file__)))
self.response_dict = {}
device_list = []
for json_file in all_devices:
if os.path.isfile('{}/api_responses/{}'.format(os.path.dirname(__file__), json_file)):
_json_file = open('{}/api_responses/{}'.format(os.path.dirname(__file__), json_file))
device_list.append(json.load(_json_file))
_json_file.close()
self.response_dict["data"] = device_list
def test_unit_is_none(self):
devices = get_devices_from_response_dict(self.response_dict, device_types.GANG)
for device in devices:
self.assertIsNone(device.unit())
class SmokeDetectorTests(unittest.TestCase):
def setUp(self):
super(SmokeDetectorTests, self).setUp()
self.api_interface = MagicMock()
all_devices = os.listdir('{}/api_responses/'.format(os.path.dirname(__file__)))
self.response_dict = {}
device_list = []
for json_file in all_devices:
if os.path.isfile('{}/api_responses/{}'.format(os.path.dirname(__file__), json_file)):
_json_file = open('{}/api_responses/{}'.format(os.path.dirname(__file__), json_file))
device_list.append(json.load(_json_file))
_json_file.close()
self.response_dict["data"] = device_list
def test_test_activated_is_false(self):
devices = get_devices_from_response_dict(self.response_dict, device_types.SMOKE_DETECTOR)
for device in devices:
self.assertFalse(device.test_activated())
def test_unit_is_none(self):
devices = get_devices_from_response_dict(self.response_dict, device_types.SMOKE_DETECTOR)
for device in devices:
if isinstance(device, WinkSmokeDetector):
self.assertIsNone(device.unit())
self.assertEqual(device.unit_type(), "boolean")
if isinstance(device, WinkCoDetector):
self.assertIsNone(device.unit())
self.assertEqual(device.unit_type(), "boolean")
if isinstance(device, WinkSmokeSeverity):
self.assertIsNone(device.unit())
self.assertIsNone(device.unit_type())
if isinstance(device, WinkCoSeverity):
self.assertIsNone(device.unit())
self.assertIsNone(device.unit_type())
class RemoteTests(unittest.TestCase):
def setUp(self):
super(RemoteTests, self).setUp()
self.api_interface = MagicMock()
all_devices = os.listdir('{}/api_responses/'.format(os.path.dirname(__file__)))
self.response_dict = {}
device_list = []
for json_file in all_devices:
if os.path.isfile('{}/api_responses/{}'.format(os.path.dirname(__file__), json_file)):
_json_file = open('{}/api_responses/{}'.format(os.path.dirname(__file__), json_file))
device_list.append(json.load(_json_file))
_json_file.close()
self.response_dict["data"] = device_list
def test_buttons_press_is_false(self):
devices = get_devices_from_response_dict(self.response_dict, device_types.REMOTE)
remote = devices[0]
self.assertFalse(remote.button_on_pressed())
self.assertFalse(remote.button_off_pressed())
self.assertFalse(remote.button_up_pressed())
self.assertFalse(remote.button_down_pressed())
def test_unit_and_capability(self):
devices = get_devices_from_response_dict(self.response_dict, device_types.REMOTE)
remote = devices[0]
self.assertIsNone(remote.unit())
self.assertEqual(remote.capability(), "opened")
class PropaneTankTests(unittest.TestCase):
def setUp(self):
super(PropaneTankTests, self).setUp()
self.api_interface = MagicMock()
all_devices = os.listdir('{}/api_responses/'.format(os.path.dirname(__file__)))
self.response_dict = {}
device_list = []
for json_file in all_devices:
if os.path.isfile('{}/api_responses/{}'.format(os.path.dirname(__file__), json_file)):
_json_file = open('{}/api_responses/{}'.format(os.path.dirname(__file__), json_file))
device_list.append(json.load(_json_file))
_json_file.close()
self.response_dict["data"] = device_list
def test_unit_and_capability(self):
devices = get_devices_from_response_dict(self.response_dict, device_types.PROPANE_TANK)
tank = devices[0]
self.assertIsNone(tank.unit())
self.assertIsNone(tank.capability())
|
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3101,
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2
] |
PreliminaryAnalysis/Interarrival Times/daily_rate_changes.py
|
alfredholmes/cryptocurrency_data_analysis
| 0 |
62255
|
<reponame>alfredholmes/cryptocurrency_data_analysis
import classification
import numpy as np
import matplotlib.pyplot as plt
from scipy.stats import expon, norm
def main():
transactions = classification.load_transactions()
orders = classification.classify_trades(transactions)
interarrivals = classification.calculate_interarrival_times(orders)
arrivals = np.cumsum(interarrivals)
time_step = 10 * 60 * 1000 #calculating hourly rates
bins = []
bin = []
time = 0
print('Processing...')
for i, t in enumerate(arrivals):
if t > time + time_step:
jump = int(np.floor((t - time) / time_step))
time += jump * time_step
bins.extend([bin + [] * (jump - 1)])
bin = []
bin.append(orders[i])
times = []
rates = []
for bin in bins:
if len(bin) != 0:
times.append(bin[0].start_time)
ia = classification.calculate_interarrival_times(bin)
loc, scale = expon.fit([i for i in ia if i > 1000])
rates.append(scale)
rate_returns = [rates[i + 1] / rates[i] for i in range(len(rates) - 1)]
hours = [[] for i in range(24 * 6)]
for i, r in enumerate(1 / np.array(rates)):
hours[i % 24 * 6].append(r)
plt.plot([i for i in range(24 * 6)], [np.mean(hour) for hour in hours])
plt.show()
if __name__ == '__main__':
main()
|
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wearebeautiful/bin/make_bundle.py
|
MarcTschudin/wearebeautiful.info
| 0 |
47422
|
#!/usr/bin/env python
import sys
sys.path.append("..")
import json
import click
import datetime
import os
import shutil
from zipfile import ZipFile
from tempfile import mkdtemp
from scale_mesh import scale_mesh
from wearebeautiful.bundles import validate_manifest, MAX_SCREENSHOT_SIZE
from wearebeautiful import model_params as param
@click.command()
@click.option('--invert/--no-invert', default=False, help='Flip the normals on the STL file')
@click.argument('lresolution', type=float)
@click.argument('mresolution', type=float)
def main(invert, lresolution, mresolution):
manifest = os.path.join("/src", param.MANIFEST_FILE)
surface = os.path.join("/src", param.SURFACE_FILE)
solid = os.path.join("/src", param.PRINT_FILE)
screenshot = os.path.join("/src", param.SCREENSHOT_FILE)
try:
with open(os.path.join("/src", manifest), "rb") as m:
j = m.read()
jmanifest = json.loads(j)
except json.decoder.JSONDecodeError as err:
print("Cannot parse manifest file. ", err)
print("This was the content that was read:")
print("%s\n" % j)
sys.exit(-1)
except IOError as err:
print("Cannot read manifest file. IO error.", err)
sys.exit(-1)
err = validate_manifest(jmanifest)
if err:
print(err)
sys.exit(-1)
id = jmanifest['id']
screenshot_size = os.path.getsize(screenshot)
if screenshot_size > MAX_SCREENSHOT_SIZE:
print("Maximum screenshot size is %d kbytes." % (MAX_SCREENSHOT_SIZE / 1024))
sys.exit(-1)
tmp_dir = mkdtemp()
low_res = os.path.join(tmp_dir, "surface-low.stl")
medium_res = os.path.join(tmp_dir, "surface-medium.stl")
if not os.path.exists(solid):
print("Cannot find solid.stl");
sys.exit(-1)
if not os.path.exists(surface):
print("Cannot find surface.stl");
sys.exit(-1)
try:
print("invert mesh", invert)
scale_mesh(invert, lresolution, surface, low_res)
scale_mesh(invert, mresolution, surface, medium_res)
except IOError as err:
print("Cannot down-scale mesh files. Error: ", err)
sys.exit(-1)
try:
shutil.copy(solid, tmp_dir)
shutil.copy(surface, tmp_dir)
shutil.copy(screenshot, tmp_dir)
except IOError as err:
print("Cannot copy files. Error: ", err)
sys.exit(-1)
dest = os.path.join("/dest", "%s-%s-%s-bundle.zip" % (id, jmanifest['bodypart'], jmanifest['pose']))
with ZipFile(dest, 'w') as zip:
zip.write(manifest, arcname="manifest.json")
zip.write(low_res, arcname="surface-low.stl")
zip.write(medium_res, arcname="surface-medium.stl")
zip.write(solid, arcname="solid.stl")
zip.write(surface, arcname="surface-orig.stl")
zip.write(screenshot, arcname="screenshot.jpg")
shutil.rmtree(tmp_dir)
if __name__ == "__main__":
main();
sys.exit(-1)
|
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] |
easyAI/games/Reversi.py
|
obravocedillo/easyAI
| 0 |
172951
|
"""
The game of Reversi. Warning: this game is not coded in an optimal
way, the AI will be slow.
"""
import numpy as np
from easyAI import TwoPlayersGame
to_string = lambda a : "ABCDEFGH"[a[0]] + str(a[1]+1)
to_array = lambda s : np.array(["ABCDEFGH".index(s[0]),int(s[1])-1])
class Reversi( TwoPlayersGame ):
"""
See the rules on http://en.wikipedia.org/wiki/Reversi
Here for simplicity we suppose that the game ends when a
player cannot play, but it would take just a few more lines to
implement the real ending rules, by which the game ends when both
players can't play.
This implementation will make a slow and dumbe AI and could be sped
up by adding a way of unmaking moves (method unmake_moves) and
coding some parts in C (this is left as an exercise :) )
"""
def __init__(self, players, board = None):
self.players = players
self.board = np.zeros((8,8), dtype=int)
self.board[3,[3,4]] = [1,2]
self.board[4,[3,4]] = [2,1]
self.nplayer=1
def possible_moves(self):
""" Only moves that lead to flipped pieces are allowed """
return [to_string((i,j)) for i in range(8) for j in range(8)
if (self.board[i,j] == 0)
and (pieces_flipped(self.board, (i,j), self.nplayer) != [])]
def make_move(self, pos):
""" Put the piece at position ``pos`` and flip the pieces that
much be flipped """
pos= to_array(pos)
flipped = pieces_flipped(self.board, pos, self.nplayer)
for i,j in flipped:
self.board[i,j] = self.nplayer
self.board[pos[0],pos[1]] = self.nplayer
def show(self):
""" Prints the board in a fancy (?) way """
print('\n'+'\n'.join([' 1 2 3 4 5 6 7 8']+ ['ABCDEFGH'[k] +
' '+' '.join([['.','1','2','X'][self.board[k][i]]
for i in range(8)]) for k in range(8)]+['']))
def is_over(self):
""" The game is considered over when someone cannot play. That
may not be the actual rule but it is simpler to code :). Of
course it would be possible to implement that a player can pass
if it cannot play (by adding the move 'pass')"""
return self.possible_moves() == []
def scoring(self):
"""
In the beginning of the game (less than 32 pieces) much
importance is given to placing pieces on the border. After this
point, only the number of pieces of each player counts
"""
if np.sum(self.board==0) > 32: # less than half the board is full
player = self.board==self.nplayer
opponent = self.board==self.nopponent
return ((player-opponent)*BOARD_SCORE).sum()
else:
npieces_player = np.sum(self.board==self.nplayer)
npieces_opponent = np.sum(self.board==self.nopponent)
return npieces_player - npieces_opponent
# This board is used by the AI to give more importance to the border
BOARD_SCORE = np.array( [[9,3,3,3,3,3,3,9],
[3,1,1,1,1,1,1,3],
[3,1,1,1,1,1,1,3],
[3,1,1,1,1,1,1,3],
[3,1,1,1,1,1,1,3],
[3,1,1,1,1,1,1,3],
[3,1,1,1,1,1,1,3],
[9,3,3,3,3,3,3,9]])
DIRECTIONS = [ np.array([i,j]) for i in [-1,0,1] for j in [-1,0,1]
if (i!=0 or j!=0)]
def pieces_flipped(board, pos, nplayer):
"""
Returns a list of the positions of the pieces to be flipped if
player `nplayer` places a piece on the `board` at position `pos`.
This is slow and could be coded in C or Cython.
"""
flipped = []
for d in DIRECTIONS:
ppos = pos + d
streak = []
while (0<=ppos[0]<=7) and (0<=ppos[1]<=7):
if board[ppos[0],ppos[1]] == 3 - nplayer:
streak.append(+ppos)
elif board[ppos[0],ppos[1]] == nplayer:
flipped += streak
break
else:
break
ppos += d
return flipped
if __name__ == "__main__":
from easyAI import Human_Player, AI_Player, Negamax
# An example: Computer vs Computer:
game = Reversi([AI_Player(Negamax(4)), AI_Player(Negamax(4))])
game.play()
if game.scoring() > 0:
print("player %d wins." % game.nplayer)
elif game.scoring() < 0:
print("player %d wins." % game.nopponent)
else:
print("Draw.")
|
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] |
rdp/__init__.py
|
emulbreh/rdp
| 0 |
49480
|
from rdp.grammar import Grammar, GrammarBuilder, ignore
from rdp.symbols import Terminal, repeat, Regexp, Optional, Lookahead
from rdp.symbols import epsilon, flatten, drop, keep
from rdp.parser import Parser
from rdp.exceptions import ParseError, LeftRecursion, InvalidGrammar, TokenizeError
|
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] |
Voicelab/toolkits/Voicelab/MeasureFormantPositionsNode.py
|
FrancisTembo/VoiceLab
| 27 |
178305
|
from Voicelab.pipeline.Node import Node
from parselmouth.praat import call
from Voicelab.toolkits.Voicelab.VoicelabNode import VoicelabNode
from Voicelab.toolkits.Voicelab.MeasurePitchNode import measure_pitch
from scipy import stats
import statistics
class MeasureFormantPositionsNode(VoicelabNode):
def __init__(self, *args, **kwargs):
"""
Args:
*args:
**kwargs:
"""
super().__init__(*args, **kwargs)
self.args = {
# 'Method': 'formants_praat_manual'
}
self.state = {
"f1_mean_pf_list": [],
"f2_mean_pf_list": [],
"f3_mean_pf_list": [],
"f4_mean_pf_list": [],
#'f1_median_pf_list': [],
#'f2_median_pf_list': [],
#'f3_median_pf_list': [],
#'f4_median_pf_list': [],
}
# On each file we want to calculate the formants at the glottal pulses
def process(self):
voice: object = self.args["voice"]
# method = self.args['Method']
# pitch = self.args['Pitch']
formant_object = self.args["Formants"]
pitch_floor = self.args["Pitch Floor"]
pitch_ceiling = self.args["Pitch Ceiling"]
pitch = measure_pitch(
voice=voice, measure="cc", floor=pitch_floor, ceiling=pitch_ceiling
)
point_process = call(
[voice, pitch], "To PointProcess (cc)"
) # Create PointProcess object
num_points = call(point_process, "Get number of points")
f1_list = []
f2_list = []
f3_list = []
f4_list = []
measurement_times = []
for point in range(0, num_points):
point += 1
t = call(point_process, "Get time from index", point)
measurement_times.append(t)
f1 = call(formant_object, "Get value at time", 1, t, "Hertz", "Linear")
f2 = call(formant_object, "Get value at time", 2, t, "Hertz", "Linear")
f3 = call(formant_object, "Get value at time", 3, t, "Hertz", "Linear")
f4 = call(formant_object, "Get value at time", 4, t, "Hertz", "Linear")
f1_list.append(f1)
f2_list.append(f2)
f3_list.append(f3)
f4_list.append(f4)
f1_list = [f1 for f1 in f1_list if str(f1) != "nan"]
f2_list = [f2 for f2 in f2_list if str(f2) != "nan"]
f3_list = [f3 for f3 in f3_list if str(f3) != "nan"]
f4_list = [f4 for f4 in f4_list if str(f4) != "nan"]
# calculate mean & median formants across pulses
if len(f1_list) > 0:
f1_mean_pf = sum(f1_list) / len(f1_list)
# f1_median_pf = statistics.median(f1_list)
else:
f1_mean_pf = "N/A"
f1_median_pf = "N/A"
if len(f2_list) > 0:
f2_mean_pf = sum(f2_list) / len(f2_list)
f2_median_pf = statistics.median(f2_list)
else:
f2_mean_pf = "N/A"
f2_median_pf = "N/A"
if len(f3_list) > 0:
f3_mean_pf = sum(f3_list) / len(f3_list)
f3_median_pf = statistics.median(f3_list)
else:
f3_mean_pf = "N/A"
f3_median_pf = "N/A"
if len(f4_list) > 0:
f4_mean_pf = sum(f4_list) / len(f4_list)
f4_median_pf = statistics.median(f4_list)
else:
f4_mean_pf = "N/A"
f4_median_pf = "N/A"
results = {}
# collect all means and median values, these will be needed at the end to calculate the formant positions
self.state["f1_mean_pf_list"].append(f1_mean_pf)
self.state["f2_mean_pf_list"].append(f2_mean_pf)
self.state["f3_mean_pf_list"].append(f3_mean_pf)
self.state["f4_mean_pf_list"].append(f4_mean_pf)
self.state["f1_median_pf_list"].append(f1_median_pf)
self.state["f2_median_pf_list"].append(f2_median_pf)
self.state["f3_median_pf_list"].append(f3_median_pf)
self.state["f4_median_pf_list"].append(f4_median_pf)
return results
# Once all of the files have been processed, we want to calculate the position across all of them
def end(self, results):
"""
Args:
results:
"""
f1_mean_pf_list = self.state["f1_mean_pf_list"]
f2_mean_pf_list = self.state["f2_mean_pf_list"]
f3_mean_pf_list = self.state["f3_mean_pf_list"]
f4_mean_pf_list = self.state["f4_mean_pf_list"]
f1_median_pf_list = self.state["f1_median_pf_list"]
f2_median_pf_list = self.state["f2_median_pf_list"]
f3_median_pf_list = self.state["f3_median_pf_list"]
f4_median_pf_list = self.state["f4_median_pf_list"]
formant_mean_lists = [
f1_mean_pf_list,
f2_mean_pf_list,
f3_mean_pf_list,
f4_mean_pf_list,
]
formant_median_lists = [
f1_median_pf_list,
f2_median_pf_list,
f3_median_pf_list,
f4_median_pf_list,
]
# append it to the results of all of them
formant_positions = self.calculate_formant_position(formant_mean_lists)
for i, result in enumerate(results):
if isinstance(formant_positions, str):
results[i][self]["Formant Position"] = formant_positions
else:
results[i][self]["Formant Position"] = float(formant_positions[i])
return results
# to calcualte the formant position we need the formants at glotal pulses for each file we rans
def calculate_formant_position(self, formant_mean_lists, formant_median_lists):
"""
Args:
formant_mean_lists:
formant_median_lists:
"""
if len(formant_mean_lists[0]) < 30: # or len(formant_medians_lists[0]) < 8:
return "Not enough samples, requires at least 30"
# Normality test for mean data
_, p_f1_mean = stats.normaltest(formant_mean_lists[0])
_, p_f2_mean = stats.normaltest(formant_mean_lists[1])
_, p_f3_mean = stats.normaltest(formant_mean_lists[2])
_, p_f4_mean = stats.normaltest(formant_mean_lists[3])
if p_f1_mean >= 0.5 or p_f2_mean >= 0.5 or p_f3_mean >= 0.5 or p_f4_mean >= 0.5:
return "formants not normally distributed"
else:
zf1_mean = stats.zscore(formant_mean_lists[0])
zf2_mean = stats.zscore(formant_mean_lists[1])
zf3_mean = stats.zscore(formant_mean_lists[2])
zf4_mean = stats.zscore(formant_mean_lists[3])
pf_mean = (zf1_mean + zf2_mean + zf3_mean + zf4_mean) / 4
return pf_mean
# normality test for median data
_, p_f1_median = stats.normaltest(formant_medians_lists[0])
_, p_f2_median = stats.normaltest(formant_medians_lists[1])
_, p_f3_median = stats.normaltest(formant_medians_lists[2])
_, p_f4_median = stats.normaltest(formant_medians_lists[3])
if (
p_f1_median >= 0.5
or p_f2_median >= 0.5
or p_f3_median >= 0.5
or p_f4_median >= 0.5
):
return "formants not normally distributed"
else:
zf1_median = stats.zscore(formant_medians_lists[0])
zf2_median = stats.zscore(formant_medians_lists[1])
zf3_median = stats.zscore(formant_medians_lists[2])
zf4_median = stats.zscore(formant_medians_lists[3])
pf_median = (zf1_median + zf2_median + zf3_median + zf4_median) / 4
return pf_median
|
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] |
astar.py
|
jeff012345/clue-part-duo
| 0 |
7299
|
<reponame>jeff012345/clue-part-duo
import heapq
from typing import List
from definitions import RoomPosition, Position
import random
import sys
class PriorityQueue:
def __init__(self):
self.elements: Array = []
def empty(self) -> bool:
return len(self.elements) == 0
def put(self, item, priority: float):
heapq.heappush(self.elements, (priority, random.randint(1, 9999999999999999), item))
def get(self):
return heapq.heappop(self.elements)[2]
def heuristic(a: Position, b: Position) -> float:
if a == b:
return 0
if isinstance(a, RoomPosition):
if isinstance(b, RoomPosition):
raise Exception("Cannot calculate heuristic between two rooms")
return 1 # (1^2 + 0^2)
if isinstance(b, RoomPosition):
return 1 # (1^2 + 0^2)
# both are Space
return (a.col - b.col) ** 2 + (a.row - b.row) ** 2
def a_star_search(start: Position, goal: Position) -> List[Position]:
if start is None:
raise Exception("Start is None")
if goal is None:
raise Exception("goal is None")
if start == goal:
raise Exception('Start and goal are the same')
frontier = PriorityQueue()
frontier.put(start, 0)
came_from: Dict[Position, Optional[Position]] = {}
cost_so_far: Dict[Position, float] = {}
came_from[start] = None
cost_so_far[start] = 0
while not frontier.empty():
current: Position = frontier.get()
if current == goal:
break
for next in current.connections:
if isinstance(next, RoomPosition) and next != goal:
# once you enter a room, it's a dead end
continue
new_cost = cost_so_far[current] + 1
if next not in cost_so_far or new_cost < cost_so_far[next]:
cost_so_far[next] = new_cost
priority = new_cost + heuristic(goal, next)
frontier.put(next, priority)
came_from[next] = current
if frontier.empty():
print(str(start) + " to " + str(goal))
raise Exception('no path found')
shortest_path = []
prev = goal
while prev is not None:
shortest_path.append(prev)
prev = came_from[prev]
shortest_path.reverse()
return shortest_path
|
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2
] |
registry/migrations/versions/0df25c7758bf_add_a_tsv_for_the_instance_contents.py
|
jbn/quilt
| 0 |
128522
|
<gh_stars>0
"""Add a tsv for the instance contents
Revision ID: <KEY>
Revises: <PASSWORD>
Create Date: 2018-03-13 14:49:51.719830
"""
from alembic import op
import sqlalchemy as sa
from sqlalchemy.dialects import postgresql
# revision identifiers, used by Alembic.
revision = '<KEY>'
down_revision = '9576b2ed4073'
branch_labels = None
depends_on = None
def upgrade():
op.add_column('instance', sa.Column('keywords_tsv', postgresql.TSVECTOR(), nullable=True))
op.create_index('idx_keywords_tsv', 'instance', ['keywords_tsv'], unique=False, postgresql_using='gin')
def downgrade():
op.drop_index('idx_keywords_tsv', table_name='instance')
op.drop_column('instance', 'keywords_tsv')
|
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2
] |
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